混沌时间序列的神经网络预测及改进算法

Xin Yin, Ye Zhou, Yi-gang He, Haixia Zhang
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引用次数: 1

摘要

本文主要提出了一种混合算法的神经网络模型,称为HAENN (hybrid algorithm Elman neural Network)。该模型基于Elman神经网络,采用一种改进的算法代替标准的BP训练算法。该改进算法将粒子群优化算法与模拟退火思想相结合,具有更快的收敛速度和更好的解质量。本文采用了Mackey-Glass混沌时间序列和Henon序列进行测试和仿真。结果表明,该模型具有较快的收敛速度、较好的稳定性、较高的预测精度和较强的可调节性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of chaotic time series of neural network and an improved algorithm
This paper mainly proposes a neural networks model with hybrid algorithm, named HAENN (Hybrid Algorithm Elman Neural Network). This model is based on Elman neural network, using an improved algorithm instead the standard BP training algorithm. This improved algorithm is combined Particle Swarm Optimization algorithm with Simulated Annealing's idea, which has faster convergence speed and better solution quality. In this paper, The Mackey-Glass chaotic time series and the Henon series are used for testing and imitating. The results indicate that by using this model can get faster convergence speed, better stability, higher the precision of prediction, and stronger adjustability.
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