更有效的非线性系统辨识新方法

Efficient approach to nonlinear system identification

介绍了一种基于量子粒子群算法构造径向基函数神经网络进行非线性系统辨识的新方法。在确定径向基函数网络的隐层结点数后,将相应网络的参数,包括隐层基函数中心、扩展常数以及输出权值和偏移编码成学习算法中的粒子个体,在全局空间中搜索具有最优适应值的参数向量。实例仿真通过和标准粒子群算法进行比较,表明了该方法的有效性和优越性。

A novel method of nonlinear system identification based on constructing radial basis function neural network using quantum. behaved particle swarm optimization algorithm is proposed. After determination of units of number in RBF layer, all parameters in relevant network such as central position, spreading constant, weightsandoffsetsofRBFNNarecodedtoparticlesinlearningalgorithm. The parameter vector, which has a best adaptation value, is searched globally. By the comparison with genetic algorithm and standard particle swarm optimization algorithm, the simulation results show the effectiveness of this method.