Artificial intelligence for cognitive modeling : theory and practice /: theory and practice. (2023)

• Record Type: Book
• Further Information: Note: Pijush Dutta, Souvik Pal, Asok Kumar, Korhan Cengiz.
• Authors: Dutta, Pijush; Pal, Souvik; (Computer scientist), Kumar, Asok; Cengiz, Korhan
• Contents: Part A: Artificial Intelligence & Cognitive Computing : Theory &Concept 1. Introduction to AI 1.1 Introduction 1.1.1 Intelligent Control 1.1.2 Expert System 1.1.3 Soft Computing 1.1.3.1 Fuzzy System 1.1.3.2 Neural Network 1.1.3.3 Genetic Algorithm 1.1.3.4 Adaptive Fuzzy Inference System 1.1.4 Real-time System Reference 2. Practical Approach of Fuzzy Logic Controller 2.1 Introduction 2.2 Classical set Properties & Operation 2.2.1 Classical Set 2.2.2 Crisp Set 2.3 Concept of Fuzzy 2.3.1 Fuzzy Set 2.3.2 Operation of Fuzzy Set 2.3.3 Properties of Fuzzy Set 2.3.4 Comparison between Crisp Set & Fuzzy set 2.3.5 Composition of Fuzzy Set 2.3.6 Properties of Fuzzy Composition 2.3.7 Classical tolerance Relation 2.3.8 Features of membership function 2.4 Fuzzification 2.4.1 Intitution 2.4.2 Inference 2.4.3 Rank Ordering 2.4.4 Angular Fuzzy set 2.4.5 Neural network 2.4.5.A Training the neural network 2.4.5.B Testing the neural network 2.4.6 Genetic Algorithm 2.4.7 Inductive reasoning 2.5 Defuzzification 2.5.1 Max – membership principle 2.5.2 Centroid method 2.5.3 Weighted average method 2.5.4 Mean- max membership or middle of maxima 2.5.5 Center of sum methods 2.5.6 Center of largest area 2.6 Example for different Defuzzification methods Reference 3. A Practical Approach to Neural Network Model 3.1 Introduction 3.1.1 Network Topology 3.1.1.A Feed forward Network 3.1.1.B. Feedback Network 3.1.2 Adjustments of Weights or Learning 3.1.2.1 Supervised Learning 3.1.2.2 Unsupervised Learning 3.1.2.3 Reinforcement Learning 3.1.3 Activation Functions 3.1.3.1 Type of Activation Function 3.1.4 Learning rules in neural network 3.1.4.1 Hebbian Learning Rule 3.1.4.2. Perceptron Learning Rule 3.1.4.3 Delta Learning Rule 3.1.4.4 Competitive Learning Rule (Winner-takes-all) 3.1.4.5 Outstar Learning Rule 3.1.5 Mcculloch Pitts neuron 3.1.6 Simple neural nets for pattern classification 3.1.7 Linear Reparability 3.1.8 Perceptron 3.2. Adaptive Linear Neuron (ADALINE) 3.2.1 Madaline (Multiple adaptive linear neurons) 3.2.2 Associative Memory Network 3.2.3 Hetero Associative memory 3.3 Bidirectional associative memory 3.4 Self-Organizing Maps: Kohonen Maps 3.5 Learning vector Quantization (LVQ) 3.6 Counter Propagation Network (CPN) 3.6.1 Full counter propagation network (FCPN) 3.6.2. Forward only counter Propagation network 3.7 ART (Adaptive resonance Theory) 3.8 Standard back propagation architecture 3.9 Boltzmann Machine Learning Reference 4. Introduction to Genetic Algorithm 4.1 Introduction 4.2 Optimization Problems 4.2.1 Steps for solving the optimization problem 4.2.2 Point to point Algorithms (P2P) 4.2.3 A∗ Search Algorithm 4.2.4 Simulated Annealing 4.2.5 Genetic Algorithm 4.2.5.1 Motivation of GA 4.2.5.2 Basic Terminology 4.2.5.3 Experiments 4.2.5.4 Parameters Tuning Technique in Genetic Algorithm 4.2.5.5 Strategy parameters 4.3 Constrained Optimization 4.4 Multimodal optimization 4.5 Multiobjective Optimization 4.6 Combinatorial Optimization 4.6.1 Differential Evolution Reference 5. Modeling of ANFIS (Adaptive Fuzzy Inference System) System 5.1 Introduction 5.2 Hybrid Systems Sequential Hybrid Systems 5.2.2. Auxiliary Hybrid Systems 5.2.3 Embedded Hybrid Systems 5.3 Neuro-Fuzzy Hybrids 5.3.2 Adaptive Neuro-Fuzzy Interference System (ANFIS) 5.3.2.1 Fuzzy Inference System (FIS) 5.3.2.2 Adaptive Network 5.4 ANFIS Architecture 5.4.1 Hybrid Learning Algorithm 5.4.2 Derivation of Fuzzy Model 5.4.2.1 Extracting the initial fuzzy model 5.4.2.2 Subtractive Clustering Technique 5.4.2.3 Grid Partitioning Technique 5.3.2.4 C- Mean Clustering Reference 6. Machine Learning Techniques for Cognitive Modeling 6.1 Introduction 6.2 Classification of Machine Learning 6.2.1 Supervised Learning 6.2.1.1 Inductive Learning 6.2.1.2 Learning by Version Space 6.2.1.3 Learning by Decision Tree (DT) 6.2.1.4 Analogical Learning 6.2.2 Unsupervised Learning 6.2.3 Reinforcement Learning 6.2.3.1 Learning Automata 6.2.3.2 Adaptive Dynamic Programming 6.2.3.3 Q learning 6.2.3.4 Temporal difference learning 6.2.4 Learning by Inductive Logic Programming (ILP) Reference Part B: Artificial Intelligence & Cognitive Computing : Practices 7. Parametric Optimization of N Channel JFET using Bio Inspired Optimization Techniques 7.1 Introduction 7.2 Mathematical Description 7.2.1 Current Equation for JFET 7.2.2 Flower Pollination Algorithm 7.2.3 Objective Function 7.3 Methodology 7.4. Result & Discussion 7.5 Conclusion Reference 8. AI based Model of Clinical and Epidemiological Factors for COVID19 8.1 Introduction 8.2 Related Work 8.3 Artificial Neural Network Based Model 8.3.1 Modeling of Artificial Neural Network 8.3.1.1 Collection, pre-processing and division of data 8.3.1.2 Implementation of neural network 8.3.2 Performance of Training, Testing & Validation of network 8.3.3 Performance evaluation of Training Functions 8.4 Results & Discussion 8.5 Conclusions Reference 9. Fuzzy Logic Based Parametric Optimization Technique of Electro Chemical Discharge Micro-Machining (µ-CDM) Process during Micro-Channel Cutting on Silica Glass 9.1 Introduction 9.2 Development of the Set up 9.3 Experimental Methodology & Result Analysis 9.3.1 Effects of process parameters on MRR, OC and MD 9.3.2 Determination of optimized condition 9.4 Conclusions References 10. Study of ANFIS model to Forecast the Average Localization Error (ALE) with Applications to Wireless Sensor Networks (WSN) 10.1 Introduction 10.2 System Model 10.2.1 Distance calculation for generalization of Optimization problem 10.2.2 Simulation Setup 10.2.3 Experimental Results and Performance Analysis 10.2.3.1 The Effect of Anchor Density 10.2.3.2 The Effect of Communication Range 10.3 Adaptive Neuro-Fuzzy Inference Architecture 10.3.1 Hybrid Learning ANFIS 10.3.2 ANFIS Training Process 10.4 Result Analysis 10.5 Conclusions References &
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2023
• Extent: 1 online resource (258 pages), illustrations (black and white)
• Subjects: 006.33; Fuzzy expert systems; Artificial intelligence -- Industrial applications -- Case studies; Neural networks (Computer science); Soft computing
• Languages: English
• ISBNs: 9781000864243; 9781000864199
• Related ISBNs: 9781032105703
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.