Introduction to neural netrworks
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
Components and structure of an RBF network
Comparing RBF networks and multilayer perceptrons
Supervised learning network
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
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
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
Unsupervised learning network
RAM-Based Neurons and Networks
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
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
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 SymbolicFuzzyand Connectionist Systems
Hybrid Systems
Working of Associative Memory
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
Limitations of Competitive Learning
Fuzzy Logic Model for Speech Recognition and LanguageUnderstanding
Associative Memory
Auto-associative Memory Model - Hopfield model
The Neocognitron
The Neocognitron
Adaptive Resonance Theor
Adaptive Resonance Theory (ART)
Competitive Learning Neural Networks
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
Associative Memory Models
Bidirectional Associative Memory (two-layer)
Bidirectional Hetero-associative Memory
Back-Propagation Network
Back-Propagation Network
Learning
Simple Learning Machines
Hidden Layer
Learning By Example
Hidden Layer Computation
Output Layer Computation
Back-Propagation Algorithm
Unit 9.Fuzzy Set Theory
Introduction to fuzzy Set
Crisp and Non-Crisp Set
Fuzzy Set
Fuzzy Membership
Fuzzy Operations
Fuzzy Properties
Fuzzy Relations
Unit 10 Fuzzy Systems
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
Branch :
Electrical and Electronics Engineering
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Subject :
Neural Networks and Fuzzy Logic
Associative Memory
Associative Memory Models
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Bidirectional Associative Memory (two-layer)
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Bidirectional Hetero-associative Memory
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