Study on a recurrent functional link-based fuzzy neural network controller with improved particle swarm optimization

2009 ◽  
Author(s):  
Zhirong Guo ◽  
Shunyi Xie ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Functional Link ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

scholarly journals Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization

2020 ◽  
Vol 10 (9) ◽  
pp. 3041
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Wall Following

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.

scholarly journals Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1302 ◽  
Author(s):  
Cheng-Jian Lin ◽  
Xin-You Lin ◽  
Jyun-Yu Jhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pendulum System ◽  
Improved Particle Swarm Optimization ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Network Controller

In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods.

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