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On the optimal design of fuzzy neural networks with robust learning for function approximation
- Source :
- IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. 30(1)
- Publication Year :
- 2008
-
Abstract
- A novel robust learning algorithm for optimizing fuzzy neural networks is proposed to address two important issues: how to reduce the outlier effects and how to optimize fuzzy neural networks, in the function approximation. This algorithm is able to reduce the outlier effects by cooperating with a conventional robust approach, and then to optimize fuzzy neural networks by determining the optimal learning rates which can minimize the next-step mean error at each iteration of our algorithm.
- Subjects :
- Mathematical optimization
Neuro-fuzzy
Computer science
Defuzzification
Fuzzy number
Electrical and Electronic Engineering
Adaptive neuro fuzzy inference system
Artificial neural network
business.industry
Deep learning
General Medicine
Computer Science Applications
Human-Computer Interaction
ComputingMethodologies_PATTERNRECOGNITION
Function approximation
Information Fuzzy Networks
Control and Systems Engineering
Outlier
Fuzzy set operations
Feedforward neural network
ComputingMethodologies_GENERAL
Artificial intelligence
Types of artificial neural networks
Intelligent control
business
Software
Information Systems
Subjects
Details
- ISSN :
- 10834419
- Volume :
- 30
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
- Accession number :
- edsair.doi.dedup.....5820e70c029652900676d85beba2e928