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In this paper, an improved Takagi-Sugeno (T-S) Fuzzy Neural Network (FNN) based on modified learning is proposed for the motion control of Autonomous Underwater Vehicles (AUV). Aiming to improve the control precision and adaptability of T-S fuzzy model, a fuzzy objective is used to update the fuzzy rules and the proportion factor on-line. A modified learning of network is developed by back-propagating...
A real-time control scheme based on fuzzy neural network (FNN) is proposed for the motion control of autonomous underwater vehicles (AUVs) in this paper, for which the dynamics of the controlled system need not be completely known. A real-time desired state planning (DSP) based a sigmoid reference model is introduced to assist the FNN to keep the track error in a low level, and that also can serve...
Fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainty in manual adjustments, a novel immune particle swarm optimization (IPSO) algorithm based on immune theory and nonlinear decreasing inertia weight (NDIW) strategy was proposed. Owing to the restraint factor and NDIW strategy, IPSO algorithm can...
Aiming at the problems that fuzzy neural network controller has heavy computation and response lag, a T-S fuzzy neural network based on hybrid learning algorithm was proposed. Immune genetic algorithm was used to optimize the parameters of membership functions off line, and the neural network was used to adjust the parameters of membership functions on line to enhance the response of the controller...
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