基于函数正交基展开的过程神经网络学习算法  

Learning Algorithms of Process Neural Networks Based on Orthogonal Function Basis Expansion

作　　者：许少华[1] 何新贵[2] 

机构地区：[1]北京航空航天大学计算机科学与工程系,北京100083 [2]北京大学信息科学技术学院,北京100871

出　　处：《计算机学报》2004年第5期645-650,共6页Chinese Journal of Computers

基　　金：国家自然科学基金 (60 3 73 10 2 );教育部博士点基金 (2 0 0 3 0 0 0 170 1)资助

摘　　要：过程神经网络的输入和连接权均可为时变函数 ,过程神经元增加了一个对于时间的聚合算子 ,使网络同时具有时空二维信息处理能力 .该文在考虑过程神经网络对时间聚合运算的复杂性的基础上 ,提出了一种基于函数正交基展开的学习算法 .在网络输入函数空间中选择一组适当的函数正交基 ,将输入函数和网络权函数都表示为该组正交基的展开形式 ,利用基函数的正交性 ,简化过程神经元对时间的聚合运算 .应用表明 ,算法简化了过程神经网络的计算复杂度 ,提高了网络学习效率和对实际问题求解的适应性 .以旋转机械故障诊断问题和油藏开发过程采收率的模拟为例验证了算法的有效性 .Both the input and link weights of process neural network can be all time-various functions, an aggregation operator on time is added to the process neuron, which provides the neural network with the capability of handling simultaneously two dimension information of time and space. In consideration of the complexity of the aggregation operation of time in process neural networks, a new learning algorithm based on function orthogonal basis expansion is proposed. Firstly a group of proper function orthogonal bases in the input function space of the neural network is selected, and then the input functions and the network weight functions are represented as expansion of the same orthogonal basis. With orthogonality of basis functions, the aggregation operation of process neurons to time is simplified. The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving. The effectiveness of the algorithm is proved in the rotation machinery fault diagnosis and the simulation in oil field development process.

关 键 词：过程神经网络 时空二维信息处理 聚合运算 函数正交基 计算复杂度 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]