共轭梯度法
差异进化
人工神经网络
非线性系统辨识
计算机科学
鉴定(生物学)
非线性系统
趋同(经济学)
感知器
算法
系统标识
数学优化
人工智能
数学
数据建模
量子力学
数据库
植物
生物
经济增长
物理
经济
作者
I. Chiha,Liouane Nouredine
标识
DOI:10.1109/iceesa.2013.6578397
摘要
A hybrid method based on Differential Evolution and Neural Network training algorithms is presented in this paper for improving the performance of neural network in the non linear system identification. For this purpose, the local optimization algorithm of conjugate gradients (CG) is combined with the differential evolution algorithm (DE), which is a population-based stochastic global search method, to yield a computationally efficient algorithm for training multilayer perceptron networks for nonlinear system identification. After, a series of simulation studies of our method on the different nonlinear systems it has been confirmed that the proposed CG+DE algorithm has yielded better identification results in terms of time of convergence and less identification error.
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