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

机译:拟议的具有FFT传递函数的神经网络估计Loranz动力学图

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The aim of this paper is to design a feed forward artificial neural network (Ann) to estimate three dimensional Loranz dynamical map by selecting an appropriate network, transfer function and node weights to get Loranz dynamical map estimation. The proposed network side by side with using Fast Fourier Transform (FFT) as transfer function is used. For different cases of the system, Deterministic, 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.
机译:本文的目的是设计一个前馈人工神经网络(Ann),通过选择合适的网络,传递函数和节点权重来估计Loranz动态图,从而估计三维Loranz动态图。使用建议的网络并排使用快速傅里叶变换(FFT)作为传递函数。对于确定性,混沌和嘈杂的系统不同情况,所提算法的实验结果将通过均方误差(MSE)与相同网络但具有传统传递函数Logsig和Tagig的结果进行经验比较。从双方,速度和准确性两方面来看,所提出算法的性能均优于其他算法。

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