一种基于粒子群优化的极限学习过程神经网络

An extreme learning process neural networks based on particle swarm optimization

本文针对过程神经元网络(Process Neural Network,PNN)模型学习参数较多,正交基展开后的梯度下降算法初值敏感、计算复杂、不易收敛等问题,结合极限学习机(Extreme Learning Machine,ELM)的快速学习特性,提出了一种新型的极限学习过程神经元网络.学习过程中摒弃梯度下降算法的迭代调整策略,采用Moore-Penrose广义逆计算输出权值矩阵.同时为弥补极限学习机由于随机赋值造成的不足,利用粒子群算法(Particle Swarm Optimization,PSO)良好的全局搜索能力进行模型参数优化,获得紧凑的网络结构,提高了模型泛化能力.仿真实验以Henon混沌时间序列和太阳黑子预测为例,验证了网络的有效性.

Aiming at the problems that process neural network has more learning parameters, sensitive to initial value, complicated computation and difficult to converge for the gradient descent algorithm based on orthogonal basis expansion, a new process neural network based on extreme learning machine is presented in this paper. The iterative adjustment strategy is rejected in the trainning process and use Moore-Penrose to calculate the output weight matrix. In order to make up for the lack of random assignment for the extreme learning machine, the particle swarm algorthim is taken and the parameters are optimized with its global search ability. This algorthim can get the more tightly network structure and improve the model generlization ability. The model and algorthim are applied to Henon chaotic time series and sunspot prediction. The simulation results confirm the validity and feasibility of the model and learning algorithm.