Table Of ContentArtificial Intelligence for
Cognitive Modeling
This book is written in a clear and thorough way to cover both the traditional and modern
uses of artificial intelligence and soft computing. It gives an in-depth look at mathemati-
cal models, algorithms, and real-world problems that are hard to solve in MATLAB. The
book is intended to provide a broad and in-depth understanding of fuzzy logic control-
lers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the
GA-ANN model.
Features
A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm,
and neural network using MATLAB)
A detailed description of the hybrid intelligent technique called the adaptive fuzzy
inference technique (ANFIS)
Formulation of nonlinear models like analysis of ANOVA and the response surface
methodology
Variety of solved problems on ANOVA and RSM
Case studies of above-mentioned intelligent techniques on the different process con-
trol systems
This book can be used as a handbook and a guide for students of all engineering disci-
plines, operational research areas, and computer applications, and for various profession-
als who work in the optimization area.
Chapman & Hall/CRC Internet of Things: Data-Centric Intelligent
Computing, Informatics, and Communication
The role of adaptation, machine learning, computational Intelligence, and data analytics in the field of
IoT Systems is becoming increasingly essential and intertwined. The capability of an intelligent sys-
tem is growing depending upon various self-decision-making algorithms in IoT Devices. IoT based
smart systems generate a large amount of data that cannot be processed by traditional data process-
ing algorithms and applications. Hence, this book series involves different computational methods
incorporated within the system with the help of Analytics Reasoning, learning methods, Artificial
intelligence, and Sense-making in Big Data, which is most concerned in IoT-enabled environment.
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Series Editors:
Souvik Pal
Sister Nivedita University, (Techno India Group), Kolkata, India
Dac-Nhuong Le
Haiphong University, Vietnam
-------------------------------------------------------------------------------
Security of Internet of Things Nodes: Challenges, Attacks, and Countermeasures
Chinmay Chakraborty, Sree Ranjani Rajendran and Muhammad Habib Ur Rehman
Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective
Meenu Gupta, Rachna Jain, Arun Solanki and Fadi Al-Turjman
Cloud IoT Systems for Smart Agricultural Engineering
Saravanan Krishnan, J Bruce Ralphin Rose, NR Rajalakshmi and N Narayanan Prasanth
Data Science for Effective Healthcare Systems
Hari Singh, Ravindara Bhatt, Prateek Thakral and Dinesh Chander Verma
Internet of Things and Data Mining for Modern Engineering and Healthcare Applications
Ankan Bhattacharya, Bappadittya Roy, Samarendra Nath Sur, Saurav Mallik and Subhasis Dasgupta
Energy Harvesting: Enabling IoT Transformations
Deepti Agarwal, Kimmi Verma and Shabana Urooj
SDN-Supported Edge-Cloud Interplay for Next Generation Internet of Things
Kshira Sagar Sahoo, Arun Solanki, Sambit Kumar Mishra, Bibhudatta Sahoo and Anand Nayyar
Internet of Things: Applications for Sustainable Development
Niranjan Lal, Shamimul Qamar, Sanyam Agarwal, Ambuj Kumar Agarwal and Sourabh Singh Verma
Artificial Intelligence for Cognitive Modeling: Theory and Practice
Pijush Dutta, Souvik Pal, Asok Kumar and Korhan Cengiz
Artificial Intelligence for
Cognitive Modeling
Theory and Practice
Pijush Dutta
Souvik Pal
Asok Kumar
Korhan Cengiz
MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the
accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products
does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular
use of the MATLAB® software.
First edition published 2023
by CRC Press
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and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 Pijush Dutta, Souvik Pal, Asok Kumar and Korhan Cengiz
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Library of Congress Cataloging-in-Publication Data
Names: Dutta, Pijush, author. | Pal, Souvik, author. | Kumar, Asok
(Computer scientist), author. | Cengiz, Korhan, author.
Title: Artificial intelligence for cognitive modeling : theory and practice / Pijush Dutta, Souvik Pal, Asok Kumar,
Korhan Cengiz.
Description: First edition. | Boca Raton : Chapman & Hall/CRC Press, 2023.
| Series: Chapman & Hall/CRC internet of things. Data-centric
intelligent computing, informatics, and communication | Includes bibliographical references and index. | Summary:
“This book is written in a clear and thorough way to cover both the traditional and modern
uses of Artificial Intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and
real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and
in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as
ANFIS and the GA-ANN model”-- Provided by publisher.
Identifiers: LCCN 2022050123 (print) | LCCN 2022050124 (ebook) | ISBN
9781032105703 (hbk) | ISBN 9781032461397 (pbk) | ISBN 9781003216001 (ebk)
Subjects: LCSH: Fuzzy expert systems. | Artificial intelligence--Industrial
applications--Case studies. | Neural networks (Computer science) | Soft computing.
Classification: LCC QA76.76.E95 D88 2023 (print) | LCC QA76.76.E95
(ebook) | DDC 006.3/3--dc23/eng/20221215
LC record available at https://lccn.loc.gov/2022050123
LC ebook record available at https://lccn.loc.gov/2022050124
ISBN: 978-1-032-10570-3 (hbk)
ISBN: 978-1-032-46139-7 (pbk)
ISBN: 978-1-003-21600-1 (ebk)
DOI: 10.1201/9781003216001
Typeset in Palatino
by SPi Technologies India Pvt Ltd (Straive)
Contents
Preface ...................................................................................................................................xiii
Acknowledgment ................................................................................................................xvii
Authors ...................................................................................................................................xix
Part A Artificial Intelligence and Cognitive Computing:
Theory and Concept .............................................................................1
1. Introduction to Artificial Intelligence .................................................................................3
1.1 Introduction ...................................................................................................................3
1.2 Intelligent Control .........................................................................................................4
1.3 Expert System ................................................................................................................5
1.4 Soft Computing Techniques ........................................................................................5
1.4.1 Fuzzy System ....................................................................................................7
1.4.1.1 Architecture of Fuzzy Logic Systems ............................................8
1.4.2 Neural Network ...............................................................................................8
1.4.2.1 Basic Architecture of Neural Network ..........................................9
1.4.3 Genetic Algorithm .........................................................................................11
1.4.4 Adaptive Neuro-Fuzzy Inference System ..................................................12
1.4.5 Real-Time System ...........................................................................................12
References ...............................................................................................................................13
2. Practical Approach of Fuzzy Logic Controller ................................................................17
2.1 Introduction .................................................................................................................17
2.2 Classical Set Properties and Operation ....................................................................17
2.2.1 Classical Set.....................................................................................................17
2.3 Properties of Crisp Sets ..............................................................................................18
2.4 Concept of Fuzziness ..................................................................................................18
2.4.1 Fuzzy Set .........................................................................................................19
2.4.2 Operation of Fuzzy Sets ................................................................................20
2.4.2.1 Union ................................................................................................20
2.4.2.2 Intersection ......................................................................................20
2.4.2.3 Complement ....................................................................................21
2.4.2.4 Difference.........................................................................................21
2.4.3 Properties of Fuzzy Sets ................................................................................21
2.4.4 Comparison between Crisp Set or Classical Set and Fuzzy Set ..............23
2.4.5 Composition of Fuzzy Set .............................................................................23
2.4.6 Properties of Fuzzy Composition ................................................................24
2.4.7 Classical Tolerance Relation .........................................................................24
2.4.8 Features of Membership Function ...............................................................24
2.4.8.1 Fuzzy Set ..........................................................................................24
v
vi Contents
2.4.8.2 Features of Fuzzy Sets ...................................................................25
2.4.8.3 Classification of Fuzzy Sets ...........................................................25
2.5 Fuzzification .................................................................................................................28
2.5.1 Institution ........................................................................................................28
2.5.2 Inference ..........................................................................................................29
2.5.3 Rank Ordering ................................................................................................31
2.5.4 Angular Fuzzy Sets ........................................................................................31
2.5.5 Neural Network .............................................................................................32
2.5.5.1 A Training the Neural Network ...................................................32
2.5.5.2 Testing the Neural Network .........................................................33
2.5.6 Genetic Algorithm .........................................................................................33
2.5.7 Inductive Reasoning ......................................................................................33
2.6 Defuzzification ............................................................................................................33
2.6.1 Lambda Cut for Fuzzy Sets/Alpha Cut .....................................................34
2.6.2 Max Membership Principle ..........................................................................35
2.6.3 Centroid Method ............................................................................................36
2.6.4 Weighted Average Method ...........................................................................37
2.6.5 Mean–Max Membership or Middle of Maxima ........................................37
2.6.6 Center of Sum Methods ................................................................................38
2.6.7 Center of Largest Area ..................................................................................39
2.7 Examples for Different Defuzzification Methods ...................................................40
2.7.1 Max Membership Method ............................................................................40
2.7.2 Centroid Method ............................................................................................40
2.7.3 Weighted Average Method ...........................................................................41
2.7.4 Mean Max Membership ................................................................................41
2.7.5 Center of Sums ...............................................................................................41
2.7.6 Center of Largest Area ..................................................................................41
References ...............................................................................................................................41
3. A Practical Approach to Neural Network Models..........................................................43
3.1 Introduction .................................................................................................................43
3.1.1 Network Topology .........................................................................................43
3.1.1.1 Feed Forward Network .................................................................44
3.1.1.2 Feedback Network .........................................................................45
3.1.2 Adjustments of Weights or Learning ..........................................................47
3.1.2.1 Supervised Learning ......................................................................47
3.1.2.2 Unsupervised Learning .................................................................48
3.1.2.3 Reinforcement Learning ................................................................48
3.1.3 Activation Functions .....................................................................................48
3.1.3.1 Type of Activation Function .........................................................49
3.1.4 Learning Rules in Neural Network .............................................................52
3.1.4.1 Hebbian Learning Rule .................................................................52
3.1.4.2 Perceptron Learning Rule .............................................................53
3.1.4.3 Delta Learning Rule .......................................................................53
3.1.4.4 Competitive Learning Rule (Winner-takes-all) ..........................54
3.1.4.5 Outstar Learning Rule ...................................................................54
3.1.5 Mcculloch Pitts Neuron ................................................................................55
3.1.6 Simple Neural Nets for Pattern Classification ...........................................55
3.1.7 Linear Reparability ........................................................................................57
Contents vii
3.1.8 Perceptron .......................................................................................................58
3.2 Adaptive Linear Neuron (Adaline) ..........................................................................59
3.2.1 Multiple Adaptive Linear Neurons (Madaline) ........................................60
3.2.2 Associative Memory Network .....................................................................61
3.2.3 Auto Associative Memory ............................................................................61
3.2.4 Hetero Associative Memory .........................................................................61
3.2.4.1 Architecture .....................................................................................61
3.3 Bidirectional Associative Memory ............................................................................62
3.4 Self-Organizing Maps: Kohonen Maps ....................................................................63
3.5 Learning Vector Quantization (LVQ) .......................................................................64
3.6 Counter Propagation Network (CPN) .....................................................................65
3.6.1 Full Counter Propagation Network (FCPN) ..............................................65
3.6.2 Forward Only Counter Propagation Network ..........................................65
3.7 Adaptive Resonance Theory (ART) ..........................................................................66
3.8 Standard Back-Propagation Architecture ................................................................68
3.9 Boltzmann Machine Learning ...................................................................................69
References ...............................................................................................................................70
4. Introduction to Genetic Algorithm ...................................................................................73
4.1 Introduction .................................................................................................................73
4.2 Optimization Problems ..............................................................................................74
4.2.1 Steps for Solving the Optimization Problem .............................................75
4.2.2 Point to Point Algorithms (P2P) ..................................................................75
4.2.3 A∗ Search Algorithm .....................................................................................76
4.2.4 Simulated Annealing .....................................................................................76
4.2.5 Genetic Algorithm (GA) ................................................................................77
4.2.5.1 Motivation of GA ............................................................................78
4.2.5.2 Basic Terminology ..........................................................................79
4.2.5.3 Experiments ....................................................................................82
4.2.5.4 Parameter Tuning Technique in Genetic
Algorithm ........................................................................................83
4.2.5.5 Strategy Parameters .......................................................................83
4.3 Constrained Optimization .........................................................................................84
4.4 Multimodal Optimization ..........................................................................................85
4.5 Multiobjective Optimization .....................................................................................86
4.6 Combinatorial Optimization .....................................................................................86
4.6.1 Differential Evolution ....................................................................................86
4.6.1.1 Suitability of DE in the Field of Optimization ...........................87
References ...............................................................................................................................87
5. Modeling of ANFIS (Adaptive Fuzzy Inference System) System ..............................91
5.1 Introduction .................................................................................................................91
5.2 Hybrid Systems ...........................................................................................................92
5.2.1 Sequential Hybrid Systems ..........................................................................92
5.2.2 Auxiliary Hybrid Systems ............................................................................92
5.2.3 Embedded Hybrid Systems ..........................................................................92
5.3 Neuro-Fuzzy Hybrids ................................................................................................92
5.3.1 Adaptive Neuro-Fuzzy Interference System (ANFIS) .............................93
5.3.1.1 Fuzzy Inference System (FIS) .......................................................93
viii Contents
5.3.1.2 Adaptive Network .........................................................................93
5.4 ANFIS Architecture .....................................................................................................94
5.4.1 Hybrid Learning Algorithm .........................................................................96
5.4.2 Derivation of Fuzzy Model ..........................................................................96
5.4.2.1 Extracting the Initial Fuzzy Model ..............................................96
5.4.2.2 Subtractive Clustering Technique ................................................97
5.4.2.3 Grid Partitioning Technique .........................................................99
5.4.2.4 C-Mean Clustering .......................................................................100
References .............................................................................................................................100
6. Machine Learning Techniques for Cognitive Modeling .............................................105
6.1 Introduction ...............................................................................................................105
6.2 Classification of Machine Learning ........................................................................105
6.2.1 Supervised Learning ...................................................................................106
6.2.1.1 Inductive Learning .......................................................................106
6.2.1.2 Learning by Version Space ..........................................................107
6.2.1.3 Learning by Decision Tree (DT) .................................................107
6.2.1.4 Analogical Learning .....................................................................108
6.2.2 Unsupervised Learning ..............................................................................108
6.2.3 Reinforcement Learning .............................................................................109
6.2.3.1 Learning Automata ......................................................................109
6.2.3.2 Adaptive Dynamic Programming .............................................110
6.2.3.3 Q-learning ......................................................................................110
6.2.3.4 Temporal Difference Learning ....................................................111
6.2.4 Learning by Inductive Logic Programming (ILP) ...................................111
6.3 Summary ....................................................................................................................111
References .............................................................................................................................112
Part B Artificial Intelligence and Cognitive Computing:
Practices ...............................................................................................115
7. Parametric Optimization of N Channel JFET Using Bio Inspired
Optimization Techniques ..................................................................................................117
7.1 Introduction ...............................................................................................................117
7.2 Mathematical Description ........................................................................................118
7.2.1 Current Equation for JFET ..........................................................................118
7.2.2 Flower Pollination Algorithm ....................................................................118
7.2.3 Objective Function .......................................................................................119
7.3 Methodology ..............................................................................................................119
7.4 Result and Discussion ..............................................................................................121
7.5 Conclusion .................................................................................................................124
References .............................................................................................................................125
8. AI-Based Model of Clinical and Epidemiological Factors
for COVID-19 ......................................................................................................................127
8.1 Introduction ...............................................................................................................127
8.2 Related Work..............................................................................................................128
8.3 Artificial Neural Network Based Model ................................................................129
Contents ix
8.3.1 Modeling of Artificial Neural Network ....................................................130
8.3.1.1 Collection, Preprocessing, and Division of Data .....................130
8.3.1.2 Implementation of Neural Network..........................................130
8.3.2 Performance of Training, Testing, and Validation of Network .............132
8.3.3 Performance Evaluation of Training Functions .......................................132
8.4 Results and Discussion .............................................................................................135
8.5 Conclusions ................................................................................................................137
References .............................................................................................................................138
9. Fuzzy Logic Based Parametric Optimization Technique of
Electro Chemical Discharge Micro-Machining (μ-CDM) Process
during Micro-Channel Cutting on Silica Glass ............................................................141
9.1 Introduction ...............................................................................................................141
9.2 Development of the Set Up ......................................................................................143
9.3 Experimental Methodology and Result Analysis .................................................143
9.3.1 Effects of Process Parameters on MRR, OC, and MD .............................145
9.3.2 Determination of Optimized Condition ...................................................148
9.4 Conclusions ................................................................................................................153
References .............................................................................................................................154
10. Study of ANFIS Model to Forecast the Average Localization Error
(ALE) with Applications to Wireless Sensor Networks (WSN) .................................157
10.1 Introduction ...............................................................................................................157
10.2 System Model ............................................................................................................159
10.2.1 Distance Calculation for Generalization of Optimization Problem .....159
10.2.2 Simulation Setup ..........................................................................................159
10.2.3 Experimental Results and Performance Analysis ...................................159
10.2.3.1 The Effect of Anchor Density ......................................................159
10.2.3.2 The Effect of Communication Range .........................................160
10.3 Adaptive Neuro-Fuzzy Inference Architecture ....................................................160
10.3.1 Hybrid Learning ANFIS .............................................................................161
10.3.2 ANFIS Training Process ..............................................................................162
10.4 Result Analysis ..........................................................................................................167
10.4.1 Grid Partition Method .................................................................................167
10.4.2 Subclustering Method .................................................................................170
10.5 Conclusions ................................................................................................................175
References .............................................................................................................................175
11. Performance Estimation of Photovoltaic Cell Using Hybrid Genetic
Algorithm and Particle Swarm Optimization ...............................................................179
11.1 Introduction ...............................................................................................................179
11.2 Mathematics Model and Objective Function of the Solar Cell ...........................180
11.2.1 Single Diode Model (SDM).........................................................................180
11.2.2 Double Diode Model (DDM) .....................................................................181
11.2.3 PV Module Model ........................................................................................182
11.3 Objective Function ....................................................................................................182
11.4 Proposed Methodology ............................................................................................183
11.4.1 Improved Cuckoo Search Optimization ...................................................183
11.5 Results and Discussion .............................................................................................184
