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Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks
- Source :
- Computers & Mathematics with Applications. 57(11-12):2009-2015
- Publication Year :
- 2009
- Publisher :
- Elsevier BV, 2009.
-
Abstract
- In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, RCQGA algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional quantum genetic algorithms (QGA) is not satisfactory. In this paper, a real-coded chaotic quantum-inspired genetic algorithm (RCQGA) is proposed based on the chaotic and coherent characters of Q-bits. In this algorithm, real chromosomes are inversely mapped to Q-bits in the solution space. Q-bits probability-guided real cross and chaos mutation are applied to the evolution and searching of real chromosomes. Chromosomes consisting of the weightings of the fuzzy neural network are coded as an adjustable vector with real number components that are searched by the RCQGA. Simulation results have shown that faster convergence of the evolution process in searching for an optimal fuzzy neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy neural network via the RCQGA are demonstrated to illustrate the effectiveness of the proposed method.
- Subjects :
- Adaptive neuro fuzzy inference system
Neuro-fuzzy
Artificial neural network
business.industry
Time delay neural network
Quantum-inspired genetic algorithm
Chaotic
Fuzzy neural
Networks chaotic
Random search
Computational Mathematics
Computational Theory and Mathematics
Modeling and Simulation
Modelling and Simulation
Genetic algorithm
Artificial intelligence
ComputingMethodologies_GENERAL
business
Intelligent control
Mathematics
Subjects
Details
- ISSN :
- 08981221
- Volume :
- 57
- Issue :
- 11-12
- Database :
- OpenAIRE
- Journal :
- Computers & Mathematics with Applications
- Accession number :
- edsair.doi.dedup.....8b95f78129db0606f9fbcbb5ac20d5bb
- Full Text :
- https://doi.org/10.1016/j.camwa.2008.10.048