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In this paper, an improved modeling based on Elman neural network was proposed to analyze the nonlinear features of nonlinear circuits with the memory effect. The input vector of the hidden layer in neural network is normalized to enhance the neural network convergence precision. A group of Chebyshev orthogonal basis functions was employed to activate hidden layer neurons. Computer simulation results of the nonlinear power amplifier (PA) have shown that the proposed behavioral modeling not only accurately describes the nonlinear distortions of PAs, but also well depicts memory effect of PAs. And, the proposed approach could be also applied to analyze linear circuits and RF power amplifiers.

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