Fuzzy Neural Network Optimization by a Particle Swarm Optimization Algorithm

2006 ◽  
pp. 752-761 ◽  
Author(s):  
Ming Ma ◽  
Li-Biao Zhang ◽  
Jie Ma ◽  
Chun-Guang Zhou
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Network Optimization ◽  
Fuzzy Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neural Network Optimization ◽  
Fuzzy Neural

scholarly journals Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuexi Peng ◽  
Kejun Lei ◽  
Xi Yang ◽  
Jinzhang Peng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Hunan Province ◽  
Swarm Optimization ◽  
Fuzzy Neural

Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi–Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

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