一种基于数值积分的过程神经元网络训练算法

Training Algorithm of Process Neural Networks Based on Numerical Integration

针对过程神经元网络的训练问题,提出了一种基于数值积分的学习算法。直接采用数值积分进行网络中动态样本与连接权函数的时域加权聚合运算,采用梯度下降法实现连接权函数特征参数及网络性质参数的调整。设计了基于梯形积分、辛普森积分、柯特斯积分等3种过程神经元网络数值积分训练方法,以太阳黑子数据预测为例进行仿真实验,结果表明,基于数值积分的过程神经元网络训练算法是有效的,其中辛普森积分算法的性能最优。

Aiming at the training problem of process neural networks,a training algorithm based on numerical integration was proposed.In proposed algorithm,the numerical integration was directly applied to deal with the weighted aggregation of dynamic samples and weight functions in time-domain,and the gradient descent method was used to adjust the weight function characteristic parameters and network property parameters.Three kinds of numerical integration methods of Trapezoidal,Simpson,and Cotes were designed.Taking the prediction of sunspot data as an example,the simulation results show that the training algorithms based on numerical integration are efficient,and the approximation performance of Simpson integration is optimal.