The document discusses the design of a neural fuzzy control system using neural networks and fuzzy logic to control complex chemical processes. It presents the structure of a neural fuzzy controller with 4 layers that implement fuzzification, rule evaluation, and defuzzification. The neural fuzzy control system is designed to have learning abilities to automate the design of fuzzy logic control systems.
Ch 05 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 5 of the book entitled "MATLAB Applications in Chemical Engineering": Numerical Solution of Partial Differential Equations. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
This presentation gives example of "Calculus of Variations" problems that can be solved analytical. "Calculus of Variations" presentation is prerequisite to this one.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
Probability formula sheet
Set theory, sample space, events, concepts of randomness and uncertainty, basic principles of probability, axioms and properties of probability, conditional probability, independent events, Baye’s formula, Bernoulli trails, sequential experiments, discrete and continuous random variable, distribution and density functions, one and two dimensional random variables, marginal and joint distributions and density functions. Expectations, probability distribution families (binomial, poisson, hyper geometric, geometric distribution, normal, uniform and exponential), mean, variance, standard deviations, moments and moment generating functions, law of large numbers, limits theorems
for more visit http://tricntip.blogspot.com/
Ch 05 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 5 of the book entitled "MATLAB Applications in Chemical Engineering": Numerical Solution of Partial Differential Equations. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
This presentation gives example of "Calculus of Variations" problems that can be solved analytical. "Calculus of Variations" presentation is prerequisite to this one.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
Probability formula sheet
Set theory, sample space, events, concepts of randomness and uncertainty, basic principles of probability, axioms and properties of probability, conditional probability, independent events, Baye’s formula, Bernoulli trails, sequential experiments, discrete and continuous random variable, distribution and density functions, one and two dimensional random variables, marginal and joint distributions and density functions. Expectations, probability distribution families (binomial, poisson, hyper geometric, geometric distribution, normal, uniform and exponential), mean, variance, standard deviations, moments and moment generating functions, law of large numbers, limits theorems
for more visit http://tricntip.blogspot.com/
We have implemented a multiple precision ODE solver based on high-order fully implicit Runge-Kutta(IRK) methods. This ODE solver uses any order Gauss type formulas, and can be accelerated by using (1) MPFR as multiple precision floating-point arithmetic library, (2) real tridiagonalization supported in SPARK3, of linear equations to be solved in simplified Newton method as inner iteration, (3) mixed precision iterative refinement method\cite{mixed_prec_iterative_ref}, (4) parallelization with OpenMP, and (5) embedded formulas for IRK methods. In this talk, we describe the reason why we adopt such accelerations, and show the efficiency of the ODE solver through numerical experiments such as Kuramoto-Sivashinsky equation.
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques S...SAJJAD KHUDHUR ABBAS
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques Simulation Strategies
3.2.3.3. Quasi-Newton (QN) Methods
These methods represent a very important class of techniques because of their extensive use in practical alqorithms. They attempt to use an approximation to the Jacobian and then update this at each step thus reducing the overall computational work.
The QN method uses an approximation Hk to the true Jacobian i and computes the step via a Newton-like iteration. That is,
SAJJAD KHUDHUR ABBAS
Ceo , Founder & Head of SHacademy
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
Random Matrix Theory and Machine Learning - Part 3Fabian Pedregosa
ICML 2021 tutorial on random matrix theory and machine learning.
Part 3 covers: 1. Motivation: Average-case versus worst-case in high dimensions 2. Algorithm halting times (runtimes) 3. Outlook
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsSean Meyn
A tutorial, and very new algorithms -- more details on arXiv and at NIPS 2017 https://arxiv.org/abs/1707.03770
Part of the Data Science Summer School at École Polytechnique: http://www.ds3-datascience-polytechnique.fr/program/
---------
2018 Updates:
See Zap slides from ISMP 2018 for new inverse-free optimal algorithms
Simons tutorial, March 2018 [one month before most discoveries announced at ISMP]
Part I (Basics, with focus on variance of algorithms)
https://www.youtube.com/watch?v=dhEF5pfYmvc
Part II (Zap Q-learning)
https://www.youtube.com/watch?v=Y3w8f1xIb6s
Big 2017 survey on variance in SA:
Fastest convergence for Q-learning
https://arxiv.org/abs/1707.03770
You will find the infinite-variance Q result there.
Our NIPS 2017 paper is distilled from this.
Introduction about Monte Carlo Methods, lecture given at Technical University of Kaiserslautern 2014.
There are many situations where Monte Carlo Methods are useful to solve data science problems
[E-book at Google Play Books] Exercises Solution Manual for MATLAB Applicatio...Chyi-Tsong Chen
This self-study solution manual in accompany with the book "MATLAB Applications in Chemical Engineering" is designed to provide readers with the key points of solving exercise problems at the end of each chapter, which therefore instructively guides readers to familiarize themselves with the related MATLAB commands and programming methods for various types of problems. Additionally, through the assistance of this solution manual, the readers would profoundly strengthen the logical abilities, problem-solving skills, and deepen the applications of MATLAB programming language to solve analysis, design, simulation and optimization problems arose in related fields of chemical engineering.
The preparation of this manual is not for directly providing solutions, but through key guidance, overview and analysis, and instructional solution-steps, to gradually cultivate readers' problem-solving skills.
[E-book at Google Play Books] MATLAB Applications in Chemical Engineering (20...Chyi-Tsong Chen
This book addresses the applications of MATLAB and Simulink in the solution of chemical engineering problems. By classifying the problems into seven different categories, the author organizes this book as follows:
Chapter One - Solution of a System of Linear Equations
Chapter Two - Solution of Nonlinear Equations
Chapter Three - Interpolation, Differentiation and Integration
Chapter Four- Numerical Solution of Ordinary Differential Equations
Chapter Five - Numerical solution of Partial Differential Equations
Chapter Six - Process Optimization
Chapter Seven - Parameter Estimation
Each chapter is arranged in four major parts. In the first part, the basic problem patterns that can be solved with MATLAB are presented. The second part describes how to apply MATLAB commands to solve the formulated problems in the field of chemical engineering. In the third and the fourth parts, exercises and summary of MATLAB instructions are provided, respectively. The description of the chemical engineering example follows the sequence of problem formulation, model analysis, MATLAB program design, execution results, and discussion. In this way, learners are first aware of the basic problem patterns and the underlying chemical engineering principles, followed by further familiarizing themselves with the relevant MATLAB instructions and programming skills. Readers are encouraged to do exercises to practice their problem-solving skills and deepen the fundamental knowledge of chemical engineering and relevant application problems.
Ch 07 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 7 of the book entitled "MATLAB Applications in Chemical Engineering": Parameter Estimation. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
Ch 06 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 6 of the book entitled "MATLAB Applications in Chemical Engineering": Process Optimization. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
Ch 04 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 3 of the book entitled "MATLAB Applications in Chemical Engineering": Numerical Solution of Ordinary Differential Equations. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
Ch 03 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 3 of the book entitled "MATLAB Applications in Chemical Engineering": Interpolation, Differentiation, and Integration. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
Ch 02 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 2 of the book entitled "MATLAB Applications in Chemical Engineering": Solution of Nonlinear Equations.
Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
Ch 01 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 1 of the book entitled "MATALB Applications in Chemical Engineering": Solution of a System of Linear Equations. Author: Prof. Chyi-Tsong Chen (陳奇中 教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
1. Intelligent Process Control
Using Neural Fuzzy Techniques
模糊類神經控制系統設計
陳奇中
Chyi-Tsong Chen
ctchen@fcu.edu.tw
Department of Chemical Engineering
Feng Chia University
逢甲大學化工系
2. Outlines
1 Introduction
2 Review on fuzzy control system:
concepts and design
3 Design of a neural fuzzy control
system for complex processes
4 Application to nonlinear chemical
process control
5 Conclusions and future prospect
3. 1 Introduction
Conventional control strategies and
limitations
• structure and design methodologies
• open-loop control
− manual control
− suitable for process whose mathematical
model is hard to characterize precisely
4. • Closed-loop control
system
− use system output error to generate control
signal
− automatic control
− widely used algorithm: PID type controller
for continuous system
⎡ 1 de ( t ) ⎤
∫0 e ( t ) dt + τ D
t
u (t ) = k c ⎢ e (t ) +
⎣ τI dt ⎥ ⎦
for discrete system
⎡ Ts k τ ⎤
u(k ) = kc ⎢e(k ) + ∑ e(i) + D (e(k ) − e(k − 1))⎥
⎣ τ I i =0 Ts ⎦
k c : proportional gain
τ I : integral time constant
τ D : derivative time constant
TS : sampling time
5. New challenges:
Extremely nonlinearities
Unmeasurable uncertainties
Unknown or imprecisely known
dynamics
Time-varying parameters
Multi-objectives
Modeling problem
Controller parameter's tuning problem
Control performance degradation
Motivation: Searching for new approaches
for complex process control
⇒ 人工智慧
Artificial Intelligence (AI)
7. 2 Conventional Fuzzy Control
System: Concept and Design
Fuzzy logic
• Fuzzy concepts and statements
Examples:
1. Ben is very tall.
2. John is a handsome boy.
3. Today is very very cold.
4. (1) Please find a man with 101 hairs and 54321
beard.
(2) Please find a bald and full beard man.
5.
⇒ Precision statement may lose meaning in some
cases.
⇒ Significance statement can reflect human's thought
and concept more naturally.
8. Classical Set Theory and Fuzzy Set
Comfortable Temperature ?
Classical Set Theory
⎧ x ∈ A, S A (x) = 1
⎨
⎩ x ∉ A, S A (x) = 0
x=15, xA(x)=1, Belongs to the set of
comfortable
x=14.9, xA(x)=0, NOT belongs to the set of
Comfortable, but belongs
to the set of cold.
⇒ Unreasonable
9. Fuzzy Set Theory (Zadeh, 1965)
Describe Fuzzy concepts and phenomena
Use membership function to represent the degree of
membership of an element in a certain fuzzy set
0 ≤ μ A (x ) ≤ 1
A: comfortable; B: cold; C: hot
μ A (15) = 0.5 degree of membership in A is 0.5
μ B (15) = 0.5 degree of membership in B is 0.5
μC (15) = 0 degree of membership in C is 0 (not belongs to)
μ A (14.9) = 0.45 degree of membership in A is 0.45
μ B (14.9) = 0.55 degree of membership in B is 0.55
μC (14.9) = 0 degree of membership in C is 0 (not belongs to)
10. Commonly used membership functions
1. Z functions
z1: μ(x,az) z2: μ(x,az,bx) z3 (Z-shape function)
2. S functions
S1: μ(x,as) S2: μ(x,as,bs) S3 (S-shape function)
13. Some typical fuzzy rules and their
reasoning methods
linguistic form formal representation
(一) If temp is high,
then add some cold water. If A then B
(二) If water level is high,
then decrease feeding rate, else
maintain the feeding rate. If A then B else C
(三) If error is large and
the error change is large,
then increase the heating rate. If A and B then C
14. Fuzzy Reasoning Methods
Type (一) If A then B
Fuzzy rule: If x=A then y=B
Now: x=A’
Conclusion: y=B’=?
B’=A’ 。(A→B)
μ B ' ( y ) = V {μ A' ( x ) ∧ [ μ A ( x ) ∧ μ B ( y )]}
x
= V {μ A' ( x ) ∧ μ A ( x )} ∧ μ B ( y )
x
= α ∧ μB ( y)
15. Type (二) If A then B else C
Fuzzy rule: If x=A then y=B else y=C
Now: x=A’
Conclusion: y=B’
"Fuzzy relation"
R = ( A × B) U ( A × C )
μ R ( x, y ) = [ μ A ( x ) ∧ μ B ( y )] ∨ [(1 − μ A ( x )) ∧ μC ( y )]
"Fuzzy implication"
B ' = A'o R
= A'o[( A × B ) U ( A × C )]
16. Type (三) If x1=A and x2=B then y=C
Fuzzy rule: If x = A and x = B then y = C
1 2
Now: x = A′ and x = B′
1 2
Conclusion: y = C ′ = ?
C ' = ( A' and B' ) o [( A and B) → C ]
μ C ' ( y) = α A ∧ α B ∧ μ C ( z )
where
α A = V ( μ A' ( x1 ) ∧ μ A ( x1 ))
x
α B = V ( μ B ' ( x2 ) ∧ μ B ( x2 ))
x
17. Multiple rules
Rule 1: If x1 = A1 and x2 = B1 then y = C1
Rule 2: If x1 = A2 and x2 = B2 then y = C2
Rule n: If x1 = An and x2 = Bn then y = Cn
Now: x1 = A′ and x2 = B′
Conclusion: y = C ′ = ?
⇒ inferring output from each rule
Ci ' = ( A' and B' ) o [( Ai and Bi ) → Ci ]
μC ' ( y ) = α A ∧ α B ∧ μC ( z )
i i i i
⇒ where
α Ai = V ( μ A' ( x1 ) ∧ μ Ai ( x1 ))
x
α B = V ( μ B ' ( x 2 ) ∧ μ B ( x 2 ))
i i
x
⇒ OUTPUT:
C ' = C1 '∪C2 '∪... ∪ Cn '
μC ' ( y ) = max(μC1 ' ( y ), μC2 ' ( y ), ..., μCn ' ( y ))
18. EXAMPLE: two rules system
μC ' ( y ) = α A ∧ α B ∧ μC ( y )
1 1 1 1
μC ' ( y ) = α A ∧ α B ∧ μC ( y )
2 2 2 2
19. Pioneer of fuzzy logical control (FLC)
E. Mamdani and S. Assilian (1974)
─ Steam Engine Control
• Input variables: pressure error (E) and the rate of pressure
error (CE)
E = P-Psp and &
CE = E
• Output variable (control input): change of heating rate (ΔU)
• Fuzzy sets: 7 linguistic terms for each variable
PB (positive big)
PM (positive medium)
PS (positive small)
ZE (zero)
NS (negative small)
NM (negative medium)
NB (negative big)
20. control rules:
extracted from operation experiences
• For examples
rule 1: IF E is PS and CE is ZE, then U is NS
rule 2: IF E is ZE and CE is ZE, then U is ZE
rule 3: IF E is PS and CE is NS, then U is NS
22. Fuzzy Control Configuration
Fuzzy Controller (模糊控制器)
+ e E
yd Inference U u
• Fuzzifier Defuzzifier plant
− e EC Engine y
(模糊化) (解模糊化) (受控系統)
de/dt (推理引擎)
Fuzzy rule Base
(規則庫)
Fuzzification
transferring crisp measured data into suitable
linguistic values (fuzzy sets)
Fuzzy rule base
store the empirical knowledge of the operation of the
process of the domain expert
Inference engine
the kernel of fuzzy logical control
simulating human decision making
Defuzzification
yield a non-fuzzy decision or control action from an
inferred fuzzy action by the inference engine
23. Features of the FLC
A model-free approach
Represent a means of
both collecting human
knowledge and expertise
Has the ability of dealing
with nonlinearities and
unknown dynamics
24. Problems of using FLC
The derivation of fuzzy rules is
often time consuming and difficult.
The system performance relies to
a great extent on so-called experts
who may not be able to transcribe
their knowledge into the requisite
rule form.
There exists no formal framework
for the choice of the parameters of
a fuzzy system.
The static fuzzy controller has no
mechanisms for adapting to real-
time plant change.
25. Motivation
⇒ Bringing the learning abilities of the
neural networks to automate and realize
the design of fuzzy logical control systems
Advantages of the combination of these
two techniques:
• The fuzzy logic systems provide a
structure framework with high-level
fuzzy IF-THEN rule thinking and
reasoning to the neural network.
• The neural networks provide the
connectionist structure (fault tolerance
and distributed representation
properties) and learning ability to the
fuzzy logical systems.
26. Comparisons of FLC, MNN and CCT
(Fukuda and Shibata, 1994)
Fuzzy N e u ra l C o n v e n tio n a l
S y s te m N e tw o rk C o n tro l
(F L C ) (M N N ) T h e o ry (C C T )
L e a rn in g A b ility B G B
K n o w le d g e G B SB
R e p r e s e n ta tio n
E x p e rt K n o w le d g e G B SB
N o n lin e a rity G G B
O p tim iz a tio n B SG SB
A b ility
F a u lt To le r a n c e G G B
Good (G); Slihtly Good (SG); Slightly Bad (SB); Bad (B)
27. Introduction to Artificial Neural
Networks
Structure of a neuron
An artificial neuron
y = f (∑ wi xi + θ )
29. A feedforward neural network
— Structure
input layer
receive signals from external environment
hidden layer
receive signals from the input layer and
transmit output signals to a subsequent layer
output layer
transmit output signals to environment
30. Operations of an artificial
neural network
1. training or learning phase
— Useinput-output data to update the
network parameters (interconnection
weights and thresholds)
2. recall phase
— Givenan input to the trained network
and then generate an output
3. generalization (prediction) phase
— Given a new (unknown) input to the
trained network and then gives a
prediction
31. Properties (advantages) of
MNN
1. It has the ability of approximating
any extremely nonlinear functions.
2. It can adapt and learn the dynamic
behavior under uncertainties and
disturbances.
3. It has the ability of fault tolerance
since the quantity and quality
informations are distributedly stored
in the weights and thresholds
between neurons.
4. It is suitable to operate in a massive
parallel framework.
32. 3 Design of a neural fuzzy
control system
Control system structure
−
yd(t) + e(t) x1 u*(t) u(t) y(t)
K3 plant
NFC +
ce(t) x2
de/dt
− ∧
y (t)
MNN
learning mechanism
34. Input-output behavior of the NFC
(1) Layer 1 (input layer)
I i(1) = xi , i = 1, 2
oi(1) = xi ,i = 1,2
(2) Layer 2 (linguistic term layer)
( xi − a i k ) 2
I ( 2)
ik =− 2
, i = 1,2; k = 1,2,L,n
bik
oi(k2) = μ Aij = exp(I i(k2) ), i = 1,2; k = 1,2,L,n
(3) Layer 3 (Rule layer)
ol( 3) = o22 ) o1 2 ) , l = 1, 2, L, n ; j = 1, 2, L, n
j
(
l
(
j
oi( 3) = μ i = I i( 3) , i = 1, 2, L , m (= n 2 )
(4) Layer 4 (Output layer)
m
I ( 4)
= ∑ o (p3) wp
p =1
I (4)
o (4)
= u∗= m
, j = 1, 2, L, m
∑ o (j3)
j =1
35. A learning algorithm for the NFC
System performance function (error function)
1
Ec = ( yd − y ) 2
2
Steepest descent algorithm
∂E
w υ ( k + 1) = w υ ( k ) − η + β Δw υ ( k )
∂ wυ
∂E
a i j ( k + 1) = a ij ( k ) − η + β Δa i j ( k )
∂ a ij
∂E
bi j (k + 1) = bi j (k ) − η + β Δbi j (k )
∂ bi j
where
∂E ∂ E ∂ y ∂ u*
=
∂ wυ ∂ y ∂ u * ∂ wυ
∂ y o (j3)
= −( y d − y )
∂ u* ∑
m
p =1
o (p3)
36. ∂E ∂ E ∂ y n
∂ u* ∂ o((3−1)n+l ∂ o1(2) ∂ I 1(2)
)
= ∑∂ o
j j j
∂ a1 j ∂ y ∂ u* l =1
( 3)
( j −1) n +l ∂ o1(2) ∂ I 1(2) ∂ a1 j
j j
∂ y 2(o1 j − a1 j ) o1 j
(w )
(1) ( 2) n
∑o ∑ o (p3) − ∑p=1 o (p3) wp ,
m m
= −( y d − y) * ( 2)
( j −1) n +l j = 1, 2, L, n
∂ u b12j (∑m o (p3) ) 2 p =1
2l
l =1
p =1
∂E ∂ y 2(o2 j − a 2 j ) o2 j
(w )
(1) ( 2) n
∑o ∑ o (3) − ∑p =1 o (p3) w p ,
m m
= −( y d − y) * ( 2)
( l −1) n + j j = 1, 2, L, n
∂ a2 j ∂ u b2 j (∑m o (p3) ) 2 p =1 p
1l
2
l =1
p =1
∂E ∂ y 2(o1 j − a1 j ) o1 j
(w )
(1) 2 (2) n
∑o ∑ o(3) − ∑p=1 o(p3) wp ,
m m
= −( yd − y) * (2)
j = 1, 2, L, n
∂ b1 j (
∂ u b3 m o(3) 2
1 j ∑p=1 p ) l =1
2l ( j −1)n+l p=1 p
And
∂E ∂ y 2(o2 j − a2 j ) o2 j
(w )
(1) 2 ( 2) n
∑o ∑ o (3) − ∑p=1 o (p3) wp ,
m m
= −( y d − y) * ( 2)
j = 1, 2, L, n
∂ b2 j (
∂ u b3 m o (3) 2
2 j ∑p =1 p ) l =1
1l ( l −1) n + j p =1 p
NOTE:
The only unknown in the learning algorithm is the
system response gradient ∂y
∂u *
⇒ MNN-based estimator
37. An MNN-based estimator (Chen and Chang, 1996)
plant
y(t)
+
u(t) −
S11
.
. j
. w2i j i
S1k .
.
. w3i
∧
S1, k +1 .
.
. y (t)
.
. MNN
.
S1, m1
Input-output behavior of the MNN
⎧ y (t − j + 1), 1≤ j ≤ k
Input layer: S1 j = ⎨
⎩u(t − j + k + 1), k + 1 ≤ j ≤ m1
m1 ~
Hidden layer: net 2i = ∑ w2i j S1 j − θ 2i ,
~ i = 1,2,L,m2
j =1
− net2i
1− e
S 2i = − net 2i
, i = 1,2,L,m2
1+ e
m2
Output layer: net3 = ∑ w3i S 2i − θ 3 ,
~
i =1
∧ ~
a (1 − e −net3 )
y=
1 + e −net3
38. A learning algorithm for the MNN-based
estimator
Error function
1 ∧
E m
= ( y − y) 2
2
Steepest descent algorithm
∧ ~ ~
~ (k + 1) = w (k ) + η ( y − y )δ w δ S + β Δ w (k )
w2ij ~ ~ ~
2ij 3 3i 2i 1 j 2ij
∧ ~ ~
~ ( k + 1) = w ( k ) + η ( y − y )δ S + β Δ w ( k )
w3i ~ ~
3i 3 2i 3i
~ ~ ∧ ~ ~
~( y − y)δ w δ + β Δθ (k )
θ 2i (k + 1) = θ 2i (k ) + η ~
3 3i 2i 2i
~ ~ ∧ ~ ~
~ ( y − y )δ + β Δ θ (k )
θ 3 (k + 1) = θ 3 (k ) + η 3 3
∧
∧
~ (k + 1) = a (k ) + η ( y − y ) y + β Δ a (k )
a ~ ~ ~ ~
~
a
where
1
δ 2i = (1 − S 2i ) (1 + S 2i )
2
∧ ∧
1~ y y
δ 3 = a (1 − ~ ) (1 + a )
~
2 a
39. System's gradient prediction
∧ ∧
∂y ∂ y ∂ y m2 ⎛ ∂ net3 ∂ S2i ∂ net2i ∂ S1, q +1 ⎞
≈
∂u
= ∑ ⎜ ∂ S ∂ net ∂ S
⎜
∂ net3 i =1 ⎝ * ⎟
⎟
∂u 1, q +1 ∂ u
* *
2i 2i ⎠
m2
= δ 3 K3 ∑ w3i δ 2i w2,i , q +1
~ ~
i =1
41. 4 Application to nonlinear process
control
An nonlinear CSTR (Ray, 1981)
Dynamic equations:
• x2
x1 = − x1 + D a (1 − x1 ) exp( )
1 + x2 / ϕ
• x2
x 2 = −(1 + δ ) x 2 + BD a (1 − x1 ) exp( ) +δu
1 + x2 / ϕ
x1 d im e n s io n le s s r e a c ta n t c o n c e n tr a tio n
x2 d im e n s io n le s s re a c to r te m p e ra tu re
u c o o lin g ja c k e t te m p e ra tu re
Da D a m k ö h le r n u m b e r
ϕ a c tiv a tio n e n e rg y
B h e a t o f re a c tio n
δ h e a t tr a n s fe r c o e ffic ie n t
Nominal system parameters (Chu et al., 1992)
Da = 0.072, ϕ = 20.0, B = 8, δ = 0.3
42. Equilibrium points
( x1 , x2 ) A = (0144,0.886)
.
(stable)
( x1 , x2 ) B = (0.445, 2.750)
(unstable)
( x1 , x2 ) c = (0.765, 4.705)
(stable)
( x1 , x2 ) A ( x1 , x2 ) B
— Control objective: →
43. Performance test and comparison
— Parameter uncertainties ( δ : 0.3 → 0.35; B :8 → 7.5)
44. — Unmeasured disturbance rejection (d=0.5)
— Handling hard input constraint ( − 2 ≤ u ≤ 2 )
46. 5 Conclusions and future
prospect
Conclusions
The advantages of the neural fuzzy control
system:
Combines the benefits of fuzzy logical system (knowledge
representation and reasoning) and neural networks (fault
tolerance and learning ability)
Provides an effective intelligent approach to complex
process control
without any a priori process knowledge
model-free direct control
be able to deal with uncertainties and nonlinearities
directly
Future prospect
stability analysis
application to multivariable process control
Self rules reduction and extraction