Neural Network & Fuzzy Systems

This book explores key topics, such as neural network architectures, learning algorithms, fuzzy logic systems, and their practical applications. Each chapter presents clear explanations, practical examples, and illustrative diagrams, ensuring that complex concepts are accessible and easy to grasp. Readers will gain valuable insights into the synergy between neural networks and fuzzy systems, equipping them to tackle real-world challenges in artificial intelligence and data analysis.

• Introduction To Neural Networks
• Network Architectures
• History Of Neural Networks
• Artificial Intelligence Of Neural Network
• Model Of A Neuron
• Genetic Algorithms
• Human Brain
• Introduction To Learning Process
• Neural Network As A Directed Graph
• Knowledge Representation
• Using Training Samples
• Gradient Optimization Procedures
• Fuzzy Systems For Knowledge Engineering
• The Concept Of Time In Neural Networks
• Network Topologies
• The Bias Neuron
• Components Of Neural Networks
• Order Of Activation
• Expert Systems
• Hebbian Learning Rule
• Training Patterns And Teaching Input
• Feed-forward Networks
• Learning Curve And Error Measurement
• Paradigms Of Learning
• Components And Structure Of An Rbf Network
• Neural Networks For Knowledge Engineering
• Comparing Rbf Networks And Multilayer Perceptrons
• Exemplary Problems Allow For Testing Self-coded Learning Strategies
• Representing Neurons
• Elman Networks
• The Perceptron, Backpropagation And Its Variants
• Weight Matrix
• Growing Rbf Networks Automatically Adjust The Neuron Density
• Quantization
• Auto Association And Traditional Application
• Combinations Of Equation System And Gradient Strategies
• Codebook Vectors
• Recurrent Perceptron-like Networks
• Initial Configuration Of A Multilayer Perceptron
• Linear Separability
• A Single Layer Perceptron
• A Multilayer Perceptron
• Back Propagation Of Error
• Resilient Backpropagation
• Hopfield Networks
• Information Processing Of An Rbf Network
• The 8-3-8 Encoding Problem
• Training Recurrent Networks
• Continuous Hopfield Networks
• Centers And Widths Of Rbf Neurons
• Heteroassociation And Analogies To Neural Data Storage
• Encoding The Information
• Learning Vector Quantization Algorithms For Supervised Learning
• Connectionist Systems For Diagnosis
• Hamming Networks
• Optimization
• Speech Processing
• Hierarchical Multimodular Network Architectures For Playing Games
• Acquisition Of Knowledge
• Architectures And Approaches To Building Connectionist Expert Systems
• The Refunn Algorithm
• Mlp For Speech Recognition
• Decision Making
• Time-delay Neural Networks For Speech Recognition
• Using Som For Phoneme Recognition
• Limitations To Using The Hopfield Network
• Counterpropagation Networks
• Neural Network Models
• Monitoring
• Representing Spatial And Temporal Patterns In Neural Networks
• Connectionist Knowledge Bases From Past Data
• Competitive Learning Neural Networks For Rules Extraction
• Pattern Associations
• Image Processing
• Neural Networks As A Problem-solving Paradigm
• Hierarchical And Modular Connectionist Systems
• Pattern Recognition And Classification
• Ram-based Neurons And Networks
• The Best Neural Network Model
• Adaptive Resonance Theory
• Kohonen Self-organizing Topological Maps
• Fuzzy Neurons
• Boltzmann Machines
• Destructive Learning
• Unsupervised Self-organizing Feature Maps
• Problem Identification And Choosing The Neural Network Model
• The Hopfield Network
• Game Playing As Pattern Recognition
• Fuzzy Neural Networks
• Combine Different Paradigms In One System
• Auto-associative Memory Model - Hopfield Model
• Nps For Knowledge-engineering
• Approximate Reasoning In Nps
• Associative Memory
• Working Of Associative Memory
• Limitations Of Competitive Learning
• Building Hybrid Connectionist Production Systems
• Hybrid Systems
• Incorporating Neural Networks Into Production Rules
• Fuzzy Logic Model For Speech Recognition And Languageunderstanding
• Artificial Intelligence Systems, Fuzzy Systems, And Neural Networks Overlap And Complement One Another
• Hybrid Systems For Speech Recognition
• The Nps Architecture
• The Neocognitron
• Bidirectional Associative Memory (Two-layer)
• Bidirectional Hetero-associative Memory
• Associative Memory Models
• Adaptive Resonance Theory Architecture
• Simple Adaptive Resonance Theory Network
• Comparison F1 And Recognition F2 Layers
• Adaptive Resonance Theory Networks
• Adaptive Resonance Theory 2
• Important Adaptive Resonance Theory Networks
• Adaptive Resonance Theory (Art)
• Competitive Learning Neural Networks
• Pattern Matching In Adaptive Resonance Theory
• Unsupervised Adaptive Resonance Theory
• Learning
• Hidden Layer
• Back-propagation Network
• Back-propagation Algorithm
• Output Layer Computation
• Simple Learning Machines
• Learning By Example
• Hidden Layer Computation
• Fuzzy Properties
• Fuzzy Relations
• Introduction To Fuzzy Set
• Fuzzy Set
• Crisp And Non-crisp Set
• Fuzzy Operations
• Fuzzy Membership
• Hybrid Systems
• Integration Of Neural Network, Fuzzy Logic & Genetic Algorithm
• Fuzzy Associative Memory
• Motion In A Crossed Electric And Magnetic Fields
• Genetic Algorithm Based On Back Propagation Network
• Neuro-genetic Hybrids
• Neuro-fuzzy Hybrid
• Fuzzy Systems
• The Lorentz Force
• Fuzzy-genetic Hybrids
• Fuzzification
• Genetic Algorithm Based Techniques For Determining Weights In A Back Propagation Network
• Defuzzification