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Proposed Neural Network with FFT Transfer Function to Estimate Henon Dynamical Map

Year: 2019

Type: article

Abstract: The aim of this paper is to design a feed forward artificial neural network (Ann) to estimate two-dimensional Henon dynamical map by selecting an appropriate network, transfer function and node weights. The proposed network side by side with using Fast Fourier Transform (FFT) as transfer function is used. For different cases of the system, chaotic and noisy, the experimental results of proposed algorithm will compared empirically, by means of the mean square error (MSE) with the results of the same network but with traditional transfer functions, Logsig and Tagsig. The performance of proposed algorithm is best from others in all cases from both sides, speed and accuracy.

Source: American Journal of Intelligent Systems

Authors: Salah H. Abid, Saad Shakir Mahmood, Yaseen A. Oraibi

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2942885990

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