應(yīng)用仿人智能魯棒性高、能對(duì)付難控對(duì)象的控制特點(diǎn)，結(jié)合模糊RBF 神經(jīng)網(wǎng)絡(luò)控制技
術(shù)，提出仿人模糊神經(jīng)網(wǎng)絡(luò)控制方法，對(duì)PID 控制器參數(shù)進(jìn)行優(yōu)化調(diào)節(jié)。該方法采用仿人智能的多模態(tài)控制思想，對(duì)PID 控制器的模式進(jìn)行選擇。對(duì)非線性系統(tǒng)控制的仿真結(jié)果表明,加入仿人控制后，系統(tǒng)從響應(yīng)速度和上升時(shí)間上均有明顯提高，具有良好的控制效果。
關(guān)鍵詞：仿人智能；模糊控制；RBF 神經(jīng)網(wǎng)絡(luò)； PID 控制；仿真
The Design And Implementation of Human-Simulated and Fuzzy Neural Network ControllerZHANG Hong (Department of Information and Control;Xi’an University of Post and Telecommunications;Xi’an 710061;china) Abstract: Human-Simulated Fuzzy Neural Network Control method is proposed, which applies with the performance of the high robust and the performance of dealing with the objects with dificulty of controlling , combines with fuzzy RBF Neural Network control technology, optimize the parameters of PID controller. The method uses multi-mode control thought of Human-Simulated intelligence, select the mode of PID controller .Simulation results in nonlinear system shows that the control performance of Human-Simulated fuzzy RBF Neural Network is better than the fuzzy RBF Neural Network .
Key words:Human-Simulated intelligence; Fuzzy control; RBF Neural Network ;PID
control;Simulation