摘要
Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.
目的 研究动态系统的神经网络辨识与控制问题． 方法 为了减小网络的固有逼近误差，提出一种新型的神经网络模型，利用动态误差反馈来修正网络输入． 结果 得到了由新型网络和稳定滤波器构成的神经网络辨识模型及基于该网络模型的自适应控制方案． 结论 理论和仿真结果都证明了该神经网络模型能够有效地应用于一类非线性系统的控制．
基金
国家自然科学基金
