Table Of Content

Artificial 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 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. 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 control 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 system 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 processing 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. This series focuses to attract researchers and practitioners who are working in Information Technology and Computer Science in the field of intelligent computing paradigm, Big Data, machine learning, Sensor data, Internet of Things, and data sciences. The main aim of the series is to make available a range of books on all aspects of learning, analytics and advanced intelligent systems and related technologies. This series will cover the theory, research, development, and applications of learning, computational analytics, data processing, machine learning algorithms, as embedded in the fields of engineering, computer science, and Information Technology. 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 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 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 Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or uti- lized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy- ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identi- fication and explanation without intent to infringe. 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