Download Soft Computing Hybrid Systems

Soft Computing
http://arf.iyte.edu.tr/~bkumova/teaching/SoftComp
Dr Bora İ Kumova
İzmir Institute of Technology; Department of Computer Engineering
Hybrid Systems
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Computational Intelligence
• hibridisation: {fuzzy; evolutionary; neural}; at least 2
• objective: bottom-up modelling smart behaviour
• approach: developed system evolves “the logic of the system”
• application: learning system; adapting/evolving the system
• artificial intelligence: top-down modelling smart behaviour
• approach: modelling the system with human logic
• application: learning expert system; adapting/evolving rule base
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Hybrid System: Properties
• learning: accumulating knowledge; probability/possibility
distribution
• explanation: tracing back from solution to causes
• adaptation: updating probability/possibility distribution
• discovery: finding new probability/possibility distribution
• flexibility: adaptive to major environment change
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Intelligent System: Learning; Explanation
learning
explanation
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Intelligent System: Adaptation; Flexibility
adaptation
flexibility
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Intelligent System: Discovery
discovery
summary
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Hybridisation: Approaches
• function-replacing: replacing concept functionality
– eg EC NN weight vector
• intercommunication: sub-problem task allocation
• fusion: melted concept; modelling fuzzy inference with NN
• transformation: information representation
– eg neural learning fuzzy rules
• combination: task integration
• association: incorporating all approaches
– eg complex system
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Hybridisation: Objectives
• adaptation: learning change
• self-oganisation: restructure knowledge; discover new interrelationships
• reinforcement: update knowledge on environmental change
• learning: accumulate knowledge
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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Conclusion
• computational intelligence: more adaptive than AI
• objective: learning to learn, explain, adapt, discover, be flexible
İYTE; Soft Computing; Spring 2011; Bora İ Kumova
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