作　　者： ; ;

机构地区： 佛山科学技术学院电子与信息工程学院

出　　处： 《计算机科学》 2015年第S1期118-121,共4页

摘　　要： 神经网络的连接权在辨识中对应于模型参数,通过权值的调节可使网络输出逼近于系统输出。将神经网络作为辨识器NNI时,经训练,网络权值即为系统参数的估计。改进算法引入加权因子是为了控制网络的输入各分量对估计值的影响程度,参数估计值总是大范围一致渐近收敛的。将网络的稳态视为某一优化的问题目标函数的极小点,由初态向稳态的收敛过程就是优化过程计算。开发了仿真程序,对具体案例进行了仿真,取得了较为理想的结果。 For identifier,the connection weight of neural network corresponds to model parameter.By adjusting weight of neural network,the network outputs are approximated to the system outputs.When taking neural networks as identifier NNI and doing some training on it,network weights will become the estimation of system parameters.The traditional algorithm is improved by introducing weighted factor so as to control the impact of estimated value made by the input factors.The estimated values of parameters are always uniformly asymptotic convergence in a wide range.The network steady state were thought as the minimum point of objective function for any optimization problem.The convergence process from the initial state to the steady state is the optimization calculation.At last,simulation experiments were developed to test some specific cases and the obtained results are ideal and reasonable.

关 键 词： 神经网络 系统辨识 系统参数 辨识模型

领　　域： [自动化与计算机技术] [自动化与计算机技术]