Jie, ShaoLi, WangWeiSong, ZhaoYaQin, ZhongMalekian, Reza2014-07-082014-07-082014Jie, S, Li, W, WeiSong, Z, YaQin, Z & Malekian, R 2014, 'Numerical analysis of modeling based on improved Elman neural network', Scientific World Journal, vol 2014, art. 271593, p. 1-12..1537-744X (online)10.1155/2014/271593http://hdl.handle.net/2263/40603A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.en© 2014 Shao Jie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Improved Elman neural network (IENN)Class-D power amplifier (CDPA)Sum of squared error (SSE)Basic Elman neural network (BENN)Behavioral modelingMemory effectNonlinear systemsArticle