Jst part2
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This document discusses neural network architectures and activation functions. It describes single and multiple layer fully connected networks as well as recurrent networks with and without hidden units. It also summarizes several common activation functions including hardlimiter, threshold, sigmoid, and hyperbolic tangent functions. It provides examples of representing Boolean functions using linear thresholds and perceptron training algorithms.
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Dokumen tersebut membahas tentang dasar-dasar pemrograman PLC menggunakan Ladder Diagram. Materi yang disampaikan mencakup penjelasan prinsip kerja PLC, komponen-komponen Ladder Diagram, dan contoh-contoh aplikasi logika kontrol menggunakan Ladder Diagram. Mahasiswa diajarkan cara memprogram PLC untuk mengendalikan sistem berdasarkan alur kerja yang direpresentasikan dalam bentuk flow chart dan diagram Ladder.
Jst part6
Back-propagation algorithm was applied to an MLP model with a XOR logic function with 2 inputs and 1 output. The sigmoid activation function was used with 2 neurons in the hidden layer and 1 neuron in the output layer. The learning rate was set to 0.5. The conclusion stated that ANN provides a practical method for learning functions over continuous and discrete attributes when an algorithmic solution cannot be formulated or when numerous examples of the required behavior are available. Two hidden layers are sufficient to solve any problem, and backpropagation and gradient descent are the main concepts.
Jst part4
This document discusses neural networks and machine learning techniques. It begins by explaining how neural networks are trained using iterative adjustments to weights to minimize error between predicted and actual outputs. Single layer perceptrons are then described as networks that connect all inputs directly to outputs, dividing feature space with a decision boundary. Multilayer perceptrons are introduced as networks with hidden layers that can solve non-linearly separable problems. Backpropagation is presented as a method for calculating partial derivatives to update weights using gradient descent to minimize error across the network.
Jst part3
The document discusses perceptron training and neural network training. It provides examples of initializing weights for an AND operation and sample input/output pairs. It also shows initializing weights for another example and calculating the summation and output for different inputs. The document discusses training a neural network by starting with random weights, loading training examples to compute the output, modifying the weights to reduce error between computed and observed outputs, iterating over all examples, and terminating when weights stop changing or error is small.
Fuzzy logic part7
Sistem fuzzy memiliki beberapa jenis kontroler logika fuzzy, seperti kontroler langsung, pengawasan, adaptasi PID, dan intervensi. Kontroler langsung menggunakan nilai keluaran sistem fuzzy sebagai variabel perintah pabrik. Kontroler pengawasan menggunakan nilai keluaran untuk menyetel kontroler PID internal. Kontroler adaptasi PID menyesuaikan parameter P, I, dan D dari kontroler PID konvensional. Kontroler intervensi bekerja berdampingan den
Fuzzy logic part6
Dokumen ini membahas desain sistem kendali fuzzy logic untuk mengatur kecepatan motor. Sistem tersebut menggunakan dua input yaitu error kecepatan motor dan perubahan error, serta satu output yaitu perubahan tegangan. Dokumen ini juga menjelaskan bentuk fungsi keanggotaan dan aturan-aturan yang digunakan pada sistem kendali fuzzy tersebut. Kesimpulannya, kendali fuzzy logic relatif mudah diimplementasikan dan semakin banyak label fungsi keang
Fuzzy logic part3
Dokumen tersebut membahas sistem berbasis aturan fuzzy yang terdiri dari tiga komponen utama: fuzzification, inference, dan defuzzification. Fuzzification mengubah masukan yang pasti menjadi fuzzy, inference melakukan penalaran menggunakan aturan fuzzy, dan defuzzification mengubah keluaran fuzzy menjadi nilai pasti.
Fuzzy logic part2
Dokumen tersebut membahas mengenai fungsi-fungsi keanggotaan yang digunakan dalam sistem fuzzy seperti fungsi sigmoid, fungsi phi, fungsi segitiga, dan fungsi trapesium. Dokumen tersebut juga menjelaskan mengenai proposisi logika fuzzy, nilai kebenaran proposisi, dan implikasi proposisi logika fuzzy.
Fuzzy logic part5
Dokumen ini membahas proses defuzzifikasi pada sistem logika fuzzy. Terdapat dua model defuzzifikasi yaitu Mamdani dan Sugeno. Model Mamdani menggunakan centroid method untuk menentukan nilai krisp keluaran, sedangkan Model Sugeno menggunakan height method dan weighted average. Kedua model digunakan untuk menghitung durasi penyiraman berdasarkan suhu dan kelembaban.
Customer ptcpi for lifting process
The document discusses the shipment process for PT Chevron Pacific Indonesia from Dumai. Crude oil is transported via pipeline from an offshore production facility to an onshore tank farm in Dumai. From there it can be shipped domestically via pipeline to a Pertamina refinery in Dumai or exported internationally via tanker.
1 n4148 1n4448
This document provides data sheets for the 1N4148 and 1N4448 high-speed switching diodes. It summarizes their key features and specifications, including maximum voltage and current ratings, packaging details, and thermal and electrical characteristics graphs. The diodes are designed for high-speed switching applications with maximum switching speeds of 4 nanoseconds.
Bridge circuits
The document discusses different types of bridge circuits. It describes the Wheatstone bridge, which converts variations in resistance into variations in voltage. When balanced, the output voltage is zero, but any change in a resistive element results in an unbalanced condition and a non-zero output voltage. It also discusses AC bridges for detecting changes in inductance and capacitance. Examples are provided for calculating voltage offset in a Wheatstone bridge and determining constants of an arm in an AC bridge. Finally, it briefly mentions comparison bridges for measuring unknown capacitance or inductance compared to known values, and the Maxwell bridge for measuring inductance in terms of a known capacitance.
Filters DAC and ADC
This document discusses various types of filters, data acquisition systems, and analog-to-digital converters (ADCs). It describes how filters are classified based on their implementation, frequency response, and element type. It also provides examples of designing low-pass, high-pass, and band-pass filters. The document outlines the basic components and functions of a data acquisition system. It explains analog-to-digital and digital-to-analog converters and provides examples of calculating their output values. Finally, it discusses different types of ADCs like successive approximation and dual-slope ADCs, along with examples of calculating their output values.
Bab 2 kontrol sekuensial PLC
1. Bab ini membahas kontrol sekuensial dan elemen-elemennya.
2. Kontrol sekuensial merupakan pengontrolan yang tergantung pada kondisi sebelumnya dan melibatkan elemen seperti saklar, relai, kontaktor, dan timer.
3. Elemen-elemen tersebut digunakan untuk merancang rangkaian kontrol sekuensial seperti diagram urutan dan diagram waktu untuk mengontrol proses secara berurutan.
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Jst part2
- 1. 66//1010//20132013 Fungsi Aktivasi • Fungsi aktivasi dengan notasi: µ(.) mendefinisikan nilai output dari suatu neuron dalam level aktivasi tertentu berdasarkan nilai output pengkombinasi linier ui. • Beberapa fungsi aktivasi yg biasa digunakan: – Hardlimiter – Threshold – Sigmoid – Tangen Hiperbolik Fungsi Aktivasi 1. Hardlimiter 2. Piecewise Linear
- 2. 66//1010//20132013 Fungsi Aktivasi 3. Threshold xx f(.)f(.) ++11 tt f(x) = 0 jika x ≤ t f(x) = 1 jika x > t Fungsi Aktivasi 4. Sigmoid 5. Tangen Hiperbolik
- 3. 6/10/20136/10/2013 Arsitektur JST Single layerSingle layer Multiple layerMultiple layer fully connectedfully connected Recurrent networkRecurrent network without hidden unitswithout hidden units inputsinputs outputsoutputs { } Recurrent networkRecurrent network with hidden unitswith hidden units Unit delayUnit delay operatoroperator Standard Activation Functions • The hard-limiting threshold function – Corresponds to the biological paradigm • either fires or not • Sigmoid functions ('S'-shaped curves) – The logistic function – The hyperbolic tangent (symmetrical) – Both functions have a simple differential – Only the shape is important )exp(1 1 )( av vf −+ =
- 4. 6/10/20136/10/2013 • Representation of Boolean function AND • Linear threshold is used Perceptron Training t = 0.0t = 0.0 YY XX WW11 = 1.5= 1.5 WW33 = 1= 1 --11 WW22 = 1= 1 11 ifif ΣΣ wwiixxii >t>t OutputOutput== {{00 otherwiseotherwise Perceptron Training • Epoch – Presentation of the entire training set to the neural network. – In the case of the AND function an epoch consists of four sets of inputs being presented to the network (i.e. [0,0], [0,1], [1,0], [1,1]) • Error – a simple definition of error – The error value is the amount by which the value output by the network differs from the target value. – For example, if we required the network to output 0 and it output a 1, then Error = -1 Sum of squaredSum of squared errors :errors :
- 5. 66//1010//20132013 Perceptron Training • Target Value (T) – Value required to be produced – If we present the network with [1,1] for the AND function, T will be 1 • Output (O) – The output value from the neuron • Ij - Inputs being presented to the neuron • Wj - Weight from input neuron (Ij) to the output neuron • LR( ) - The learning rate This dictates how quickly the network converges It is set by a matter of experimentation η Perceptron Training • Algorithm While epoch produces a non null errorWhile epoch produces a non null error End WhileEnd While