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Hybrid identification in fuzzy-neural networks

机译：模糊神经网络中的混合辨识

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摘要

This paper introduces an identification method for nonlinear models in the form of Fuzzy-Neural Networks (FNN). In this model, we use two forms of the fuzzy inference methods―a simplified and linear fuzzy inference, and exploit a standard Error Back Propagation learning algorithm. The FNN modeling and identification environment realizes parameter identification through a synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a Hard C-Means (HCM) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out by combining both genetic optimization (genetic algorithm, GA) and the improved complex method. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model with sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process and NO_x emission process data of gas turbine power plant).
机译：本文以模糊神经网络（FNN）的形式介绍了一种非线性模型的辨识方法。在该模型中，我们使用两种形式的模糊推理方法（简化和线性模糊推理），并采用标准的误差反向传播学习算法。 FNN建模和识别环境通过聚类技术，遗传优化和复杂的搜索方法的协同使用来实现参数识别。我们使用Hard C-Means（HCM）聚类算法来确定此模糊模型中使用的信息颗粒的隶属函数的初始顶点。然后使用混合算法调整诸如隶属函数的顶点，学习率和动量系数之类的参数。提出的混合识别算法是结合遗传优化（遗传算法，GA）和改进的复杂方法来实现的。引入具有加权因子的总目标函数（性能指标），以在模型的逼近和泛化之间实现合理的平衡。通过选择和调整该目标函数的加权因子，我们揭示了如何设计具有声音逼近和泛化能力的模型。使用多个时间序列数据（燃气炉，污水处理过程和燃气轮机电厂的NO_x排放过程数据）对提出的模型进行了实验。

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