Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model
Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model
复制标题
DOI：
10.1016/j.eswa.2014.10.040
复制
发表时间：
2015-04-01
影响因子：
8.5
通讯作者：
George, Nithin V.
中科院分区：
文献类型：
--
作者：
Gotmare, Akhilesh;Patidar, Rohan;George, Nithin V.
本文尝试用Hammerstein模型对非线性系统进行建模。本文所考虑的Hammerstein模型是一个具有自适应无限脉冲响应(IIR)滤波器的级联函数型人工神经网络(FLANN)。为了避免传统的梯度下降训练策略导致的局部最优问题，采用了一种新近提出的随机算法布谷鸟搜索算法(CSA)对模型进行训练。将所提方案的建模精度与其他流行的进化计算算法对Hammerstein模型的建模精度进行了比较。仿真结果表明，基于CSA的方案具有更强的建模能力。(C)2014爱思唯尔有限公司。保留所有权利。
An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results. (C) 2014 Elsevier Ltd. All rights reserved.