一种时变输入输出过程神经元网络及学习算法研究  

Process neural networks with time-varying inputs and outputs and learning algorithm

作　　者：许少华[1] 何新贵[2] 王兵[1] 

机构地区：[1]大庆石油学院计算机与信息技术学院,黑龙江大庆163318 [2]北京大学信息科学技术学院,北京100871

出　　处：《控制与决策》2007年第12期1425-1428,共4页Control and Decision

基　　金：国家自然科学基金项目(60373102;60473051);教育部博士点基金项目(20030001701)

摘　　要：针对输入/输出均为时变函数的非线性系统建模问题,提出一种时变输入输出过程神经元网络模型,并给出了具体的学习算法.过程神经元网络的输入、输出均可为时变函数,其空间、时间聚合算子分别取为空间加权求和及含时间变参积分,聚合运算和激励能同时反映时变输入信号的空间聚合作用和输入过程中的阶段时间累积效应.仿真实验结果验证了所提出模型和算法的有效性.Aiming at the nonlinear system modeling problem that input and output are time-varying functions, a process neural network model with time-varying inputs and outputs is proposed, and the concrete learning algorithm is given. The inputs and outputs of the proposed process neural network are all time-varying functions. Its spatial- temporal aggregation operators are adopted as space weighted summation and integral depending on time parameter separately. Aggregation operation and activation can reflect the space aggregation function of the time-varying input signals and the stage time additive effect in the input process at the same time. The simulation experiment results show the effectiveness of the proposed model and algorithm.

关 键 词：时变输入输出系统 过程神经元网络 聚合算子 学习算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]