一种基于过程神经网络的动态系统控制信号求解模型和算法

摘要

针对非线性动态系统控制问题,提出了一种基于过程神经网络的控制信号求解模型和算法.利用过程神经网络对动态系统时变输入/输出信号的非线性映射机制和对系统过程模态特征的自适应提取能力,建立基于过程神经网络的辨识模型；然后根据所建立的辨识模型、系统控制结构和状态参数之间的关系,构建可满足系统信息传递约束关系的控制信号求解模型.分析了过程神经网络控制模型的信息处理机制,给出了基于GA与LMS相结合的优化求解算法,实验结果验证了模型和算法的有效性.%Aiming at the control problem of nonlinear dynamic system, a control signal solving model and algorithm based on Process Neural Network (PNN) was proposed. First,a system forward identification model based on PNN was set up by using nonlinear transform mechanism and self-adaptive learning ability of PNN to time-varying input-output signals of dynamic system, then according to the established model, the system control structure and the expected output signals,a control signal solving model and algorithm which satisfies system dynamic signal transform mechanism and transfer constraint relation was constructed. The information processing mechanism based on PNN control model was analyzed and the control signal optimize method based on GA coupled with LMS was given. The experiment results veri-fy the feasibility of the model and algorithm.