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Training of Fuzzy Neural Networks via Quantum- Behaved Particle Swarm Optimization and Rival Penalized Competitive Learning

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Saeed Farzi , STIKOM Dinamika Bangsa Jambi
Dibuat : 2012-06-23, dengan 1 file

Keyword : Fuzzy radial basis function, rival penalized competitive learning, and particle swarm optimization.
Subjek : Training of Fuzzy Neural Networks via Quantum- Behaved Particle Swarm Optimization and Rival Penalized Competitive Learning
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.4/2784-2.pdf
Sumber pengambilan dokumen : Internet

There are some difficulties encountered in the application of fuzzy radial basis function neural network. One of


them is how to determine the number of hidden (rule) neurons and another difficulty is about interpretability. In order to


overcome these difficulties, we have proposed a fuzzy neural network based on radial basis function network and takagi-


sugeno fuzzy system. We have used a new structure of fuzzy radial basis function neural network, which has been proved that


it is better than other structures in term of interpretability. Our model also uses rival penalized competitive learning and a


swarm based algorithm called quantum-behaved particle swarm optimization to determine design parameters of hidden layer


and design parameters of output layer, respectively-rival penalized competitive learning is the best clustering algorithm that is


introduced so far. The particle swarm optimization is a well-known population-based swarm intelligence algorithm. The


quantum-behaved particle swarm optimization is also proposed by combining the classical particle swarm optimization


philosophy and quantum mechanics to improve performance of particle swarm optimization. We have compared the


performance of the proposed method with gradient based method. Simulation results of nonlinear function approximation


demonstrate the superiority of the proposed method over gradient based method.

Deskripsi Alternatif :

There are some difficulties encountered in the application of fuzzy radial basis function neural network. One of


them is how to determine the number of hidden (rule) neurons and another difficulty is about interpretability. In order to


overcome these difficulties, we have proposed a fuzzy neural network based on radial basis function network and takagi-


sugeno fuzzy system. We have used a new structure of fuzzy radial basis function neural network, which has been proved that


it is better than other structures in term of interpretability. Our model also uses rival penalized competitive learning and a


swarm based algorithm called quantum-behaved particle swarm optimization to determine design parameters of hidden layer


and design parameters of output layer, respectively-rival penalized competitive learning is the best clustering algorithm that is


introduced so far. The particle swarm optimization is a well-known population-based swarm intelligence algorithm. The


quantum-behaved particle swarm optimization is also proposed by combining the classical particle swarm optimization


philosophy and quantum mechanics to improve performance of particle swarm optimization. We have compared the


performance of the proposed method with gradient based method. Simulation results of nonlinear function approximation


demonstrate the superiority of the proposed method over gradient based method.

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