Neural Network and Fuzzy Logics
Introduction to neural netrworks
Supervised learning network and unsupervised learning network
Hybrid SymbolicFuzzyand Connectionist Systems
Adaptive Resonance Theory and Associative Memory
Back-Propagation Network and The Neocognitron
Fuzzy Set Theory and Fuzzy Systems
- Introduction to Neural Networks
- History of neural networks
- Network architectures
- Artificial Intelligence of neural network
- Knowledge Representation
- Human Brain
- Model of a neuron
- Neural Network as a Directed Graph
- The concept of time in neural networks
- Components of neural Networks
- Network Topologies
- The bias neuron
- Representing neurons
- Order of activation
- Introduction to learning process
- Paradigms of learning
- Training patterns and Teaching input
- Using training samples
- Learning curve and error measurement
- Gradient optimization procedures
- Exemplary problems allow for testing self-coded learning strategies
- Hebbian learning rule
- Genetic Algorithms
- Expert systems
- Fuzzy Systems for Knowledge Engineering
- Neural Networks for Knowledge Engineering
- Feed-forward Networks
- The perceptron, backpropagation and its variants
- A single layer perceptron
- Linear Separability
- A multilayer perceptron
- Resilient Backpropagation
- Initial configuration of a multilayer perceptron
- The 8-3-8 encoding problem
- Back propagation of error
- Components and structure of an RBF network
- Information processing of an RBF network
- Combinations of equation system and gradient strategies
- Centers and widths of RBF neurons
- Growing RBF networks automatically adjust the neuron density
- Comparing RBF networks and multilayer perceptrons
- Recurrent perceptron-like networks
- Elman networks
- Training recurrent networks
- Hopfield networks
- Weight matrix
- Auto association and traditional application
- Heteroassociation and analogies to neural data storage
- Continuous Hopfield networks
- Quantization
- Codebook vectors
- Adaptive Resonance Theory
- Kohonen Self-Organizing Topological Maps
- Unsupervised Self-Organizing Feature Maps
- Learning Vector Quantization Algorithms for Supervised Learning
- Pattern Associations
- The Hopfield Network
- Limitations to using the Hopfield network
- Boltzmann Machines
- Neural Network Models
- Hamming Networks
- Counterpropagation Networks
- RAM-Based Neurons and Networks
- Fuzzy Neurons
- Fuzzy Neural Networks
- Hierarchical and Modular Connectionist Systems
- Neural Networks as a Problem-Solving Paradigm
- Problem Identification and Choosing the Neural Network Model
- Encoding the Information
- The Best Neural Network Model
- Architectures and Approaches to Building Connectionist Expert Systems
- Connectionist Knowledge Bases from Past Data
- Neural Networks Can Memorize and Approximate Fuzzy Rules
- Acquisition of Knowledge
- Destructive Learning
- Competitive Learning Neural Networks for Rules Extraction
- The REFuNN algorithm
- Representing Spatial and Temporal Patterns in Neural Networks
- Pattern Recognition and Classification
- Image Processing
- Speech processing
- MLP for Speech Recognition
- Using SOM for Phoneme Recognition
- Time-Delay Neural Networks for Speech Recognition
- Monitoring
- Connectionist Systems for Diagnosis
- Optimization
- Decision Making
- Game Playing as Pattern Recognition
- Hierarchical Multimodular Network Architectures for Playing Games
- Hybrid Systems
- Artificial Intelligence Systems, Fuzzy Systems, and Neural Networks Overlap and Complement One Another
- Combine Different Paradigms in One System
- Incorporating Neural Networks into Production Rules
- Building Hybrid Connectionist Production Systems
- The NPS Architecture
- Approximate Reasoning in NPS
- NPS for Knowledge-Engineering
- Hybrid Systems for Speech Recognition
- Fuzzy Logic Model for Speech Recognition and LanguageUnderstanding
- Limitations of Competitive Learning
- Associative Memory
- Working of Associative Memory
- Auto-associative Memory Model - Hopfield model
- Adaptive Resonance Theory (ART)
- Competitive Learning Neural Networks
- Limitations of Competitive Learning
- Adaptive Resonance Theory Networks
- Simple Adaptive Resonance theory Network
- Important Adaptive Resonance Theory Networks
- Unsupervised Adaptive Resonance Theory
- Adaptive Resonance Theory Architecture
- Comparison F1 and Recognition F2 layers
- Pattern Matching in Adaptive Resonance Theory
- Adaptive Resonance Theory 2
- Associative Memory Models
- Bidirectional Associative Memory (two-layer)
- Bidirectional Hetero-associative Memory
- Introduction to fuzzy Set
- Crisp and Non-Crisp Set
- Fuzzy Set
- Fuzzy Membership
- Fuzzy Operations
- Fuzzy Properties
- Fuzzy Systems
- Fuzzification
- Defuzzification
- Integration of Neural Network, Fuzzy Logic & Genetic Algorithm
- Hybrid Systems
- Neuro-Fuzzy Hybrid
- Neuro-Genetic Hybrids
- Fuzzy-Genetic Hybrids
- Genetic Algorithm based on Back Propagation Network
- Genetic Algorithm based techniques for determining weights in a Back Propagation Network
- Fuzzy Associative Memory
- Fuzzy Relations