Soft Computing integrating Evolutionary Neural and Fuzzy Systeme Neuro-fuzzy Systems Xinbo Gao School of Electronic Engineering Xidian University 2004,10
Neuro-fuzzy Systems Xinbo Gao School of Electronic Engineering Xidian University 2004,10
Introduction Neuro-fuzzy systems Soft computing methods that combine in various ways neural networks and fuzzy concepts ANN-nervous system -low level perceptive and signal integration Fuzzy part-represents the emergent higher level"reasoning aspects
Introduction ◼ Neuro-fuzzy systems ◼ Soft computing methods that combine in various ways neural networks and fuzzy concepts ◼ ANN – nervous system – low level perceptive and signal integration ◼ Fuzzy part – represents the emergent “higher level” reasoning aspects
Introduction Fuzzy Neural Networks Fuzzy Neural Sets Networks Membership functions and Rule learning ■“Fuzzification"of neural networks Endowing of fuzzy system with neural learning features
Introduction ◼ “Fuzzification” of neural networks ◼ Endowing of fuzzy system with neural learning features
Introduction Co-operative-neural algorithm adapt fuzzy systems Off-line -adaptation On-line-algorithms are used to adapt as the system operates ■( Concurrent -where the two techniques are applied after one another as pre-or post-processing Hybrid-fuzzy system being representedas a network structure, making it possible to take advantage of learning algorithm inherited from ANNs
Introduction ◼ Co-operative-neural algorithm adapt fuzzy systems ◼ Off-line – adaptation ◼ On-line – algorithms are used to adapt as the system operates ◼ Concurrent – where the two techniques are applied after one another as pre- or post-processing ◼ Hybrid – fuzzy system being represented as a network structure, making it possible to take advantage of learning algorithm inherited from ANNs
Fuzzy Neural Networks Introduction of fuzzy concepts into artificial neurons and neural networks For example,while neural networks are good at recognizing patterns, they are not good at explaining how they reach their decisions. Fuzzy logic systems,which can reason with imprecise information,are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. These limitations have been a central driving force behind the creation of intelligent hybrid systems where two or more techniques are combined in a manner that overcomes individual techniques
Fuzzy Neural Networks ◼ Introduction of fuzzy concepts into artificial neurons and neural networks ◼ For example, while neural networks are good at recognizing patterns, they are not good at explaining how they reach their decisions. ◼ Fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. ◼ These limitations have been a central driving force behind the creation of intelligent hybrid systems where two or more techniques are combined in a manner that overcomes individual techniques