softComputingintegratirgEioluconaryFeuradl.andFuzzySysteNeuro-fuzzy SystemsXinbo GaoSchool of Electronic EngineeringXidian University2004,10
Neuro-fuzzy Systems Xinbo Gao School of Electronic Engineering Xidian University 2004,10
Introduction Neuro-fuzzy systems Soft computing methods that combine invarious ways neural networks and fuzzyconcepts ANN - nervous system - low levelperceptive 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
IntroductionFuzzyNeuralNetworksNeuralFuzzySetsNetworksMembershipfunctionsandRuleleaming"Fuzzification"ofneuralnetworksEndowing of fuzzy system with neural learningfeatures
Introduction ◼ “Fuzzification” of neural networks ◼ Endowing of fuzzy system with neural learning features
Introduction Co-operative-neural algorithm adapt fuzzy systemsOff-line -adaptation On-line - algorithms are used to adapt as the system operatesConcurrent - where the two techniques are applied after oneanother as pre- or post-processingHybrid - fuzzy system being represented as a network structure,making it possible to take advantage oflearning algorithm inheritedfromANNs
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 NetworksIntroductionof fuzzy concepts into artificial neurons and neuralnetworksFor 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, aregood at explaining their decisions but they cannot automaticallyacquire the rulesthey use to make those decisions.These limitations have been a central driving force behind the creationof intelligent hybrid systems where two or more techniques arecombined inamannerthatovercomes 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