基于随机神经网络的模糊建模预测方法研究

Systems Analysis Modelling Simulation Pub Date : 2003-12-01 DOI:10.1080/0232929032000115074

Li Bo, Zhang Shi-ying, W. Xiufeng

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引用次数: 4

摘要

本文提出了一种基于修正Takagi和Sugeno (MTS)模糊模型与随机神经网络相结合的新型建模预测方法。提出了期望最大化(EM)算法来计算神经网络结构参数及其权重。理论分析和预测实例均表明，该技术具有较强的通用性，方法是有效的。

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Research on fuzzy modeling forecasting method based on stochastic neural network

In this article, a novel modeling forecasting method based on the combination of the Modified Takagi and Sugeno (MTS) fuzzy model and the stochastic neural network is presented. Expectation–Maximization (EM) algorithm is put forward to calculate the parameters of neural network structure and its weights. Theoretical analysis and prediction examples all show that the technique has strong universalized capabilities and the methods are effective.

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Systems Analysis Modelling Simulation

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