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Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation

机译：基于规则的多FNN辨识与进化模糊粒化

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摘要

In this paper, we introduce a category of Multi-Fuzzy-Neural Networks (Multi-FNNs) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs are based on a concept of fuzzy rule-based FNNs that use H ard C-M cans (HCM) clustering and evolutionary fuzzy granulation and exploit linear inference being treated as a generic inference mechanism of approximate reasoning. By this nature, this FNN model is geared toward capturing relationships between information granules-fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership functions) becomes an important design feature of the FNN model that contributes to its structural and parametric optimization. The genetically guided global optimization is then augmented by more refined gradient-based learning mechanisms such as a standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the experimental data, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates, and momentum coefficients) are adjusted using genetic algorithms. The proposed aggregate performance index helps achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate an effectiveness of the introduced model, several numeric data sets are experimented with. Those include a time-series data of gas furnace, NOx emission process of gas turbine power plant and some synthetic data.
机译：在本文中，我们介绍了一类多模糊神经网络（Multi-FNN）模型，分析了基础架构，并提出了一个综合的识别框架。所提出的Multi-FNN基于基于模糊规则的FNN概念，该概念使用Hard C-M罐（HCM）聚类和进化模糊粒度，并将线性推理用作近似推理的通用推理机制。通过这种性质，此FNN模型适用于捕获信息颗粒-模糊集之间的关系。信息颗粒本身的形式（尤其是它们的分布和隶属函数的类型）成为FNN模型的重要设计特征，这有助于其结构和参数优化。然后，通过更精细的基于梯度的学习机制（例如标准反向传播）来增强遗传指导的全局优化。 HCM算法的作用是执行实验数据的预处理，用于确定Multi-FNN的结构。使用遗传算法调整Multi-FNN的详细参数（例如隶属函数的顶点，学习率和动量系数）。拟议的综合性能指标有助于在模型的逼近能力和泛化（预测）能力之间实现合理的平衡。为了评估引入模型的有效性，尝试了几个数值数据集。其中包括燃气炉的时间序列数据，燃气轮机电厂的NOx排放过程以及一些综合数据。

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