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A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks
- Source :
- FUZZ-IEEE
- Publication Year :
- 2008
- Publisher :
- IEEE, 2008.
-
Abstract
- This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
- Subjects :
- Artificial neural network
business.industry
Cultural algorithm
Computer Science::Neural and Evolutionary Computation
Particle swarm optimization
Fuzzy control system
Machine learning
computer.software_genre
Fuzzy logic
Evolutionary computation
Identification (information)
Artificial intelligence
Multi-swarm optimization
business
computer
Mathematics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)
- Accession number :
- edsair.doi...........96b2e2d2e68fedb8cb9932dc3bb4cf3c