This document provides an overview of the NeuroSolutions software. It includes sections on product information, getting started, terms to know, menus and toolbars, component palettes, command toolbars, views, help, user options, examples, simulations, and concepts. The document introduces the key features and functionality of the software.
Iskemia adalah suatu keadaan kekurangan oksigen yang bersifat sementara dan reversibel.
Iskemia yang lama akan menyebabkan kematian otot atau nekrosis.
Secara klinis nekrosis miokardium dikenal dengan nama infark miokard
Iskemia adalah suatu keadaan kekurangan oksigen yang bersifat sementara dan reversibel.
Iskemia yang lama akan menyebabkan kematian otot atau nekrosis.
Secara klinis nekrosis miokardium dikenal dengan nama infark miokard
Salam Untuk Semua Kami Menyediakan CD KUMPULAN PENELITIAN TINDAKAN KELAS DAN PENELITIAN TINDAKAN SEKOLAH dan PTS yang bisa digunakan sebagai referensi dalam pembuatan karya ilmiah dan juga sebagai referensi untuk kenaikan pangkat, DVD Penelitian Tindakan Kelas Ini untuk Tingkat TK, SD, SMP, SMA Sederajat dengan Format Word, sangat bagus untuk menambah Khazanah Keilmuan Kita Semuanya. Semoga Bermanfaat. Tersedia juga CD RPP Kurikulum 2013 Revisi 2017 dengan mengintegrasikan PPK, Literasi, 4C (Creative, Criticalthinking, Communicative, dan Collaborative) dan Hots Semester 1 dan Semester 2 SKL (Standar Kompetensi Kelulusan) KI&KD (Kompetensi Inti dan Kompetensi Dasar) Silabus Pembelajaran RPP (Rencana Pelaksanaan Pembelajaran Pembelajaran) KKM (Kriteria Ketuntasan Minimal) BONUS : Kode Etik Guru Ikrar Guru Tata Tertib Guru Alokasi Waktu Jurnal Agenda Guru Kalender Pendidikan Program Tahunan Program Semester Pemesanan Hubungi 085797510051
Berikut adalah contoh judul-judul dalam Penelitian Ttindakan Kelas :
1. Upaya Meningkatkan Gairah Belajar Siswa Dalam Pembelajaran IPS Dengan Menggunakan Media Gambar
2. Upaya Meningkatkan Motivasi Belajar Siswa SD Melalui Peranan Hadiah Sebagai Perangsang Timbulnya Kompetensi
3. Upaya Meningkatkan Kedisplinan Siswa Melalui Penerapan Hukuman
4. Upaya Meminimalkan Miskonsepsi Dan Meningkatkan Pemahaman Konsep-Konsep IPA Melalui Pembelajaran Konstruktivistik Bagi Siswa Kelas IV SD
5. Upaya Meningkatkan Prestasi Belajar IPA di Sd Dengan Pendekatan Ketrampilan Proses
6. Upaya Mengatasi Kesulitan Belajar Melalui Pemberian Bimbingan Belajar
7. Peningkatan Kedisplinan Siswa Melalui Keteladanan Guru SD
8. Upaya Meningkatkan Pembelajaran Fisika Pada Sekolah SLTP Melalui Optimalisasi Kegiatan
9. Laboratorium Berbasis Cooperative Learning Sebagai Implementasi KBK
10. Peranan Bertanya Siswa SD Dalam Meningkatkan Proses Belajar Mengajar Matematika
11. Peranan Penggunaan Metode Ceramah Dan Tanya Jawab Terhadap Peningkatan Prestasi Belajar Mata Pelajaran IPS Terpadu Kelas VI SD
12. Melalui Pembelajaran Kooperatif Learning Untuk Meningkatkan Minat Belajar Siswa Kelas IV SD
13. Meningkatkan Motivasi Belajar Matematika Dengan Strategi Pakem Kelas IV SD
14. Upaya Meningkatkan Minat Dan Motivasi Belajar Pada Mata Pelajaran IPS Melalui Metode Ceramah Bervariasi Siswa Kelas V SD
15. Peningkatan Pembelajaran Matematika Di SD Melalui Penggunaan Alat Peraga Secara Efektif
16. Upaya Meningkatkan Motivasi Belajar Ipa Melalui Pendekatan Eksploratory Discovery
17. Peranan Media Dalam Meningkatkan Ketrampilan Membaca Di Kelas Rendah
18. Upaya Menumbuhkan Bakat Dan Kreativitas Siswa kelas IV SD Dalam Pembelajaran Matematika Melalui Metode Discovery Learning
19. Upaya Meningkatkan Minat Belajar Siswa Dalam Pembelajaran Matematika Dengan Menggunakan Metode Laboratory
Salam Untuk Semua Kami Menyediakan CD KUMPULAN PENELITIAN TINDAKAN KELAS DAN PENELITIAN TINDAKAN SEKOLAH dan PTS yang bisa digunakan sebagai referensi dalam pembuatan karya ilmiah dan juga sebagai referensi untuk kenaikan pangkat, DVD Penelitian Tindakan Kelas Ini untuk Tingkat TK, SD, SMP, SMA Sederajat dengan Format Word, sangat bagus untuk menambah Khazanah Keilmuan Kita Semuanya. Semoga Bermanfaat. Tersedia juga CD RPP Kurikulum 2013 Revisi 2017 dengan mengintegrasikan PPK, Literasi, 4C (Creative, Criticalthinking, Communicative, dan Collaborative) dan Hots Semester 1 dan Semester 2 SKL (Standar Kompetensi Kelulusan) KI&KD (Kompetensi Inti dan Kompetensi Dasar) Silabus Pembelajaran RPP (Rencana Pelaksanaan Pembelajaran Pembelajaran) KKM (Kriteria Ketuntasan Minimal) BONUS : Kode Etik Guru Ikrar Guru Tata Tertib Guru Alokasi Waktu Jurnal Agenda Guru Kalender Pendidikan Program Tahunan Program Semester Pemesanan Hubungi 085797510051
Berikut adalah contoh judul-judul dalam Penelitian Ttindakan Kelas :
1. Upaya Meningkatkan Gairah Belajar Siswa Dalam Pembelajaran IPS Dengan Menggunakan Media Gambar
2. Upaya Meningkatkan Motivasi Belajar Siswa SD Melalui Peranan Hadiah Sebagai Perangsang Timbulnya Kompetensi
3. Upaya Meningkatkan Kedisplinan Siswa Melalui Penerapan Hukuman
4. Upaya Meminimalkan Miskonsepsi Dan Meningkatkan Pemahaman Konsep-Konsep IPA Melalui Pembelajaran Konstruktivistik Bagi Siswa Kelas IV SD
5. Upaya Meningkatkan Prestasi Belajar IPA di Sd Dengan Pendekatan Ketrampilan Proses
6. Upaya Mengatasi Kesulitan Belajar Melalui Pemberian Bimbingan Belajar
7. Peningkatan Kedisplinan Siswa Melalui Keteladanan Guru SD
8. Upaya Meningkatkan Pembelajaran Fisika Pada Sekolah SLTP Melalui Optimalisasi Kegiatan
9. Laboratorium Berbasis Cooperative Learning Sebagai Implementasi KBK
10. Peranan Bertanya Siswa SD Dalam Meningkatkan Proses Belajar Mengajar Matematika
11. Peranan Penggunaan Metode Ceramah Dan Tanya Jawab Terhadap Peningkatan Prestasi Belajar Mata Pelajaran IPS Terpadu Kelas VI SD
12. Melalui Pembelajaran Kooperatif Learning Untuk Meningkatkan Minat Belajar Siswa Kelas IV SD
13. Meningkatkan Motivasi Belajar Matematika Dengan Strategi Pakem Kelas IV SD
14. Upaya Meningkatkan Minat Dan Motivasi Belajar Pada Mata Pelajaran IPS Melalui Metode Ceramah Bervariasi Siswa Kelas V SD
15. Peningkatan Pembelajaran Matematika Di SD Melalui Penggunaan Alat Peraga Secara Efektif
16. Upaya Meningkatkan Motivasi Belajar Ipa Melalui Pendekatan Eksploratory Discovery
17. Peranan Media Dalam Meningkatkan Ketrampilan Membaca Di Kelas Rendah
18. Upaya Menumbuhkan Bakat Dan Kreativitas Siswa kelas IV SD Dalam Pembelajaran Matematika Melalui Metode Discovery Learning
19. Upaya Meningkatkan Minat Belajar Siswa Dalam Pembelajaran Matematika Dengan Menggunakan Metode Laboratory
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MFG/PRO QAD Reporting Framework Document GuideVinh Nguyen
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
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The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
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Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
1. Table of Contents
Preface 32
About On-line Help ................................................................................. 32
Acknowledgments .................................................................................. 32
Product Information 33
Contacting NeuroDimension.................................................................... 33
NeuroSolutions Technical Support ........................................................... 34
NeuroDimension Products and Services .................................................. 35
Level Restrictions ................................................................................... 39
Evaluation Mode..................................................................................... 40
NeuroSolutions Pricing ........................................................................... 40
NeuroSolutions University Site License Pricing ......................................... 41
Ordering Information ............................................................................... 41
Getting Started 42
System Requirements............................................................................. 42
Running the Demos ................................................................................ 42
What to do after Running the Demos ....................................................... 45
Frequently Asked Questions (FAQ) ......................................................... 45
Terms to Know....................................................................................... 50
Main Window ................................................................................... 50
Inspector.......................................................................................... 51
Breadboards .................................................................................... 52
Neural Components.......................................................................... 53
Toolbars and Palettes ....................................................................... 53
Selection and Stamping Modes ......................................................... 54
Temporary License........................................................................... 54
Menus & Toolbars .................................................................................. 55
File Menu & Toolbar Commands ....................................................... 55
Edit Menu & Toolbar Commands ....................................................... 56
Alignment Menu & Toolbar Commands .............................................. 57
Windows Menu & Toolbar Commands .............................................. 58
Component Menu ............................................................................. 59
Tools ............................................................................................... 59
Tools Menu Commands .............................................................. 59
Control Menu & Toolbar Commands ............................................ 61
Macro Menu & Toolbar Commands ............................................. 62
Customize Toolbars Page ........................................................... 63
Component Palettes......................................................................................................................................63
Command Toolbars .......................................................................................................................................64
Customize Buttons Page............................................................. 65
View ................................................................................................ 66
View Menu ................................................................................. 66
Macro Bars ................................................................................ 66
Status Bar.................................................................................. 68
Help................................................................................................. 69
Help Menu & Toolbar Commands ................................................ 69
Activate Software Panel .............................................................. 70
User Options .......................................................................................... 71
Options Window ............................................................................... 71
Options Workspace Page.................................................................. 72
1
2. Options Save Page........................................................................... 73
Examples ............................................................................................... 74
Example 1 - Toolbar Manipulation...................................................... 74
Example 2 - Component Manipulation................................................ 74
Example 3 - Inspecting a Component's Parameters ............................ 75
Simulations 76
Simulations ............................................................................................ 76
Introduction to Neural Network Simulations .............................................. 77
What Are Artificial Neural Networks ................................................... 77
A Prototype Problem......................................................................... 78
Ingredients of a Simulation ...................................................................... 79
Formulation of the problem................................................................ 79
Data Collection and Coding ............................................................... 79
Getting Data into the Network ............................................................ 80
Cross Validation ............................................................................... 81
Network Topology ............................................................................ 82
Network Training .............................................................................. 84
Probing ............................................................................................ 86
Running the Simulation ..................................................................... 86
Concepts 86
Concepts ............................................................................................... 86
NeuroSolutions Structure ........................................................................ 87
NeuroSolutions Structure .................................................................. 87
Palettes ........................................................................................... 87
Breadboard...................................................................................... 88
NeuroSolutions Graphical User Interface (GUI) ........................................ 89
NeuroSolutions Graphical User Interface (GUI) .................................. 89
Logic of the Interface........................................................................ 89
Components .................................................................................... 90
The Inspector................................................................................... 90
Single-Click vs. Double-Click............................................................. 91
File Open Dialog Box ........................................................................ 92
Save As Dialog Box .......................................................................... 92
Toolbars and Palettes ....................................................................... 92
Title Bar........................................................................................... 93
Scroll Bars ....................................................................................... 93
Network Construction ....................................................................... 94
Network Construction ................................................................. 94
Stamping ................................................................................... 94
Manipulating Components........................................................... 94
Replacing Axons and Synapses .................................................. 95
Connectors ................................................................................ 95
Cabling ...................................................................................... 96
Stacking..................................................................................... 97
Network Access ............................................................................... 97
Network Access ......................................................................... 97
Probes ....................................................................................... 98
Data Input/Output ....................................................................... 99
Transmitters and Receivers ........................................................100
Network Simulation..........................................................................100
Network Simulation....................................................................100
Application Window Commands .......................................................100
2
3. Size command (System menu) 100
Move command (Control menu) 101
Minimize command (application Control menu)............................101
Maximize command (System menu) 101
Close command (Control menus) 101
Restore command (Control menu) 102
Switch to command (application Control menu) 102
Generating Source Code .......................................................................103
Generating Source Code .................................................................103
Customized Components .......................................................................103
Customized Components .................................................................103
Testing the Network ...............................................................................104
The TestingWizard ..........................................................................104
Freezing the Network Weights..........................................................104
Cross Validation ..............................................................................104
Production Data Set.........................................................................105
Sensitivity Analysis..........................................................................105
Confusion Matrix .............................................................................105
Correlation Coefficient .....................................................................106
ROC Matrix .....................................................................................107
Performance Measures ....................................................................108
Practical Simulation Issues.....................................................................109
Practical Simulation Issues...............................................................109
Associating a File Extension with an Editor........................................110
Data Preparation .............................................................................110
Normalization File............................................................................111
Forms of Backpropagation ...............................................................112
Probing ...........................................................................................113
Saving and Fixing Network Weights..................................................113
Weights File ....................................................................................113
Saving Network Data .......................................................................118
Stop Criteria ....................................................................................118
Constructing Learning Dynamics ......................................................118
Simulating Recurrent Networks ........................................................119
Component Naming Conventions .....................................................119
Coordinating Unsupervised and Supervised Learning ........................121
Organization of NeuroSolutions ..............................................................121
Organization of NeuroSolutions ........................................................121
Activation Family .............................................................................122
Activation Family .............................................................................122
Axon Family ..............................................................................124
MemoryAxon Family ..................................................................125
FuzzyAxon Family .....................................................................127
ErrorCriteria Family .........................................................................128
Synapse Family.........................................................................130
Backprop Family..............................................................................131
Backprop Family..............................................................................131
BackAxon Family.......................................................................132
BackMemoryAxon Family...........................................................134
BackSynapse Family .................................................................136
3
4. GradientSearch Family ....................................................................137
GradientSearch Family ..............................................................137
Controls Family ...............................................................................139
Controls Family .........................................................................139
ActivationControl Family ............................................................140
BackpropControl Family.............................................................142
Unsupervised Family .......................................................................143
Unsupervised Family .................................................................143
Hebbian Family .........................................................................144
Competitive Family ....................................................................145
Kohonen Family ........................................................................146
Probe Family...................................................................................147
Probe Family.............................................................................147
Input Family ....................................................................................148
Input Family ....................................................................................148
Transmitter Family...........................................................................149
Transmitter Family...........................................................................149
Schedule Family..............................................................................150
Schedule Family..............................................................................150
Introduction to Neural Computation 151
Introduction to NeuroComputation ..........................................................151
Introduction to Neural Computation.........................................................152
Introduction to NeuroComputation ....................................................152
History of Neural Networks...............................................................152
What are Artificial Neural Networks...................................................153
Neural Network Solutions .................................................................154
Neural Network Analysis ........................................................................155
Neural Network Analysis ..................................................................155
Neural Network Taxonomies ............................................................157
Learning Paradigms.........................................................................159
Learning Paradigms...................................................................159
Cost Function ............................................................................160
Gradient Descent ......................................................................161
Constraining the Learning Dynamics.................................................166
Constraining the Learning Dynamics...........................................166
Practical Issues of Learning ...................................................................168
Practical Issues of Learning .............................................................168
Training Set ....................................................................................168
Network Size...................................................................................169
Learning Parameters .......................................................................169
Stop Criteria ....................................................................................170
Unsupervised Learning ..........................................................................171
Unsupervised learning .....................................................................171
Support Vector Machines .......................................................................174
Support Vector Machines .................................................................174
Dynamic Networks.................................................................................176
Dynamic Networks...........................................................................176
Famous Neural Topologies ....................................................................177
Famous Neural Topologies ..............................................................177
Perceptron ......................................................................................177
Multilayer Perceptron .......................................................................178
Madaline .........................................................................................180
Radial Basis Function Networks .......................................................180
Associative Memories ......................................................................181
Jordan/Elman Networks ...................................................................182
4
5. Hopfield Network .............................................................................183
Principal Component Analysis Networks ...........................................184
Kohonen Self-Organizing Maps (SOFM) ...........................................185
Adaptive Resonance Theory (ART) ..................................................187
Fukushima ......................................................................................187
Time Lagged Recurrent Networks.....................................................187
Tutorials 190
Tutorials Chapter ...................................................................................190
Running NeuroSolutions ........................................................................190
Signal Generator Example .....................................................................191
Signal Generator Example ...............................................................191
Construction Rules ..........................................................................192
Stamping Components ....................................................................193
On-line Help ....................................................................................193
Connectors .....................................................................................193
Selecting and Configuring a Component ...........................................194
Arranging Icons ...............................................................................194
Connecting Components..................................................................195
The Cursor......................................................................................195
Component Compatibility .................................................................195
Bringing in the Function Generators ..................................................196
Stacking Components......................................................................196
Accessing the Component Hierarchy ................................................197
Access Points .................................................................................198
Displaying the Output Waveform ......................................................198
Opening the Display Window............................................................199
Controlling Data Flow ......................................................................199
Configuring the Controller ................................................................199
Running the Signal Generator Example ............................................200
Things to Try with the Signal Generator ............................................201
What You have Learned from the Signal Generator Example .............202
Combination of Data Sources Example ...................................................203
Combination of Data Sources Example .............................................203
Constructing a McCulloch-Pitts Processing Element ..........................203
Preparing Files for Input into NeuroSolutions .....................................204
Things to Try with the Combination of Data Sources Example ............206
What You have Learned from the Combination of Data Sources Example207
The Perceptron and Multilayer Perceptron ..............................................207
Perceptron and Multilayer Perceptron Example .................................207
Perceptron Topology .......................................................................207
Constructing the Learning Dynamics of a Perceptron .........................208
Alternate Procedure for Constructing the Learning Dynamics of a Perceptron 210
Selecting the Learning Paradigm......................................................211
Running the Perceptron ...................................................................212
MLP Construction ............................................................................213
Running the MLP .............................................................................214
Things to Try with the Perceptron and Multilayer Perceptron Example 215
What You have Learned from the Perceptron and Multilayer Perceptron Example
......................................................................................................216
Associator Example ...............................................................................217
Associator Example .........................................................................217
Building the Associator ....................................................................217
Things to Try with the Associator ......................................................221
What you have Learned from the Associator Example .......................221
Filtering Example...................................................................................222
5
6. Filtering Example.............................................................................222
Constructing A Linear Filter..............................................................222
Things to Try with the Linear Filter....................................................224
Adaptive Network Construction.........................................................225
Running the Adaptive Network .........................................................225
Things to Try with the Adaptive Network ...........................................226
What You have Learned from the Filter Example ...............................229
Recurrent Neural Network Example ........................................................229
Recurrent Neural Network Example ..................................................229
Creating the Recurrent Topology ......................................................229
Fixed Point Learning ........................................................................231
Running the Recurrent Network ........................................................232
Things to Try with the Recurrent Network ..........................................234
What You have Learned from the Recurrent Network Example...........237
Frequency Doubler Example ..................................................................237
Frequency Doubler Example ............................................................237
Creating the Frequency Doubler Network ..........................................237
Configuration of the Trajectory..........................................................239
Running the Frequency Doubler Network ..........................................239
Using the Gamma Model to Double the Frequency ............................240
Visualizing the State Space..............................................................242
Things to Try with the Frequency Doubler Network ............................244
What You have Learned from the Frequency Doubler Example ..........246
Unsupervised Learning Example ............................................................247
Unsupervised Learning Example ......................................................247
Introduction to Unsupervised Learning ..............................................247
Noise Reduction with Oja's or Sanger's Learning ...............................248
Things to Try with the Unsupervised Network ....................................249
What You have Learned from the Unsupervised Learning Example ....251
Principle Component Analysis Example ..................................................251
Principal Component Analysis Example ............................................251
Introduction to Principal Component Analysis....................................251
Running the PCA Network ...............................................................251
Things to Try with the PCA Network..................................................253
What You have Learned from the Principal Component Analysis Example 253
Competitive Learning Example...............................................................253
Competitive Learning Example.........................................................253
Introduction to Competitive Learning .................................................253
Constructing the Competitive Network ..............................................254
Things to Try with the Competitive Network .......................................256
What You have Learned from the Competitive Learning Example.......258
Kohonen Self Organizing Feature Map (SOFM) Example .........................259
Kohonen Self Organizing Feature Map (SOFM) Example ...................259
Introduction to SOFM Example .........................................................259
SOFM Network Construction ............................................................259
Running the SOFM Network .............................................................260
Things to Try with the SOFM Network ...............................................261
What you have Learned from the Kohonen SOFM Example ...............261
Character Recognition Example .............................................................261
Character Recognition Example .......................................................261
Introduction to Character Recognition Example .................................262
Constructing the Counterpropagation Network...................................262
Running the Counterpropagation Network .........................................263
Things to Try with the Counterpropagation Network ...........................264
What You have Learned from the Character Recognition Example .....265
Pattern Recognition Example .................................................................265
6
7. Pattern Recognition Example ...........................................................265
Introduction to Pattern Recognition Example .....................................266
Constructing the Pattern Recognition Network ...................................266
Running the Pattern Recognition Network .........................................268
What you have Learned from the Pattern Recognition Example..........269
Time Series Prediction Example.............................................................269
Time Series Prediction Example.......................................................269
Introduction to Time Series Prediction Example .................................269
Constructing the TLRN Network .......................................................270
Running the TLRN Network ..............................................................271
What You have Learned from the Time Series Prediction Example .....271
Neural Network Components 271
Components .........................................................................................271
Engine Family .......................................................................................272
Activation Family ...................................................................................272
Axon Family ....................................................................................272
Axon.........................................................................................272
BiasAxon ..................................................................................273
CombinerAxon ..........................................................................274
GaussianAxon...........................................................................275
LinearAxon................................................................................276
LinearSigmoidAxon ...................................................................277
LinearTanhAxon........................................................................278
NormalizedAxon........................................................................279
NormalizedSigmoidAxon ............................................................280
SigmoidAxon.............................................................................281
SoftMaxAxon.............................................................................282
TanhAxon .................................................................................283
ThresholdAxon..........................................................................284
WinnerTakeAllAxon ...................................................................285
Access Points ...........................................................................286
Axon Family Access Points........................................................................................................................286
DLL Implementation...................................................................287
Axon DLL Implementation..........................................................................................................................287
BiasAxon DLL Implementation...................................................................................................................288
GaussianAxon DLL Implementation.........................................................................................................288
LinearAxon DLL Implementation...............................................................................................................289
LinearSigmoidAxon DLL Implementation.................................................................................................290
LinearTanhAxon DLL Implementation......................................................................................................291
SigmoidAxon DLL Implementation............................................................................................................292
SoftMaxAxon DLL Implementation............................................................................................................292
TanhAxon DLL Implementation.................................................................................................................293
ThresholdAxon DLL Implementation........................................................................................................294
WinnerTakeAllAxon DLL Implementation ................................................................................................295
Examples ..................................................................................296
Axon Example...............................................................................................................................................296
BiasAxon Example.......................................................................................................................................297
GaussianAxon Example..............................................................................................................................298
LinearAxon Example...................................................................................................................................299
LinearSigmoidAxon Example.....................................................................................................................300
LinearTanhAxon Example..........................................................................................................................301
SigmoidAxon Example................................................................................................................................302
SoftMaxAxon Example................................................................................................................................303
TanhAxon Example.....................................................................................................................................304
ThresholdAxon Example.............................................................................................................................305
WinnerTakeAllAxon Example.....................................................................................................................306
Macro Actions ...........................................................................307
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