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Neural network and fuzzy systems
  • Online Study
  • Computer Science and Engineering
  • Neural network and fuzzy systems

Want to study about neural network and fuzzy system. Here is the list of chapters on neural network and fuzzy system chose your topics and start learning from today.

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
  • 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 SymbolicFuzzyand Connectionist Systems
  • 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
The Neocognitron
  • The Neocognitron
Adaptive Resonance Theory
  • 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
  • Linear Associator Model
  • 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
Fuzzy Set Theory
  • Introduction to fuzzy Set
  • Crisp and Non-Crisp Set
  • Fuzzy Set
  • Fuzzy Membership
  • Fuzzy Operations
  • Fuzzy Properties
  • Fuzzy Relations
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
  • The Lorentz Force
  • Motion in a crossed electric and magnetic fields