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首页> 外文期刊>Applied Soft Computing >Complex neural fuzzy system and its application on multi-class prediction - A novel approach using complex fuzzy sets, IIM and multi-swarm learning
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Complex neural fuzzy system and its application on multi-class prediction - A novel approach using complex fuzzy sets, IIM and multi-swarm learning

机译：复杂的神经模糊系统及其对多级预测的应用 - 一种使用复杂模糊集，IIM和多群学习的新方法

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In this paper, we present a novel complex neural fuzzy approach to multi-class prediction. The complex neural fuzzy system (CNFS) is proposed using complex fuzzy sets (CFSs), fuzzy causalities and multi-swarm machine learning. In general, CFSs are regarded as advanced fuzzy sets with membership degrees defined in the unit disk of the complex plane, in contrast to regular fuzzy sets with membership degrees in the real-valued unit interval [0,1]. The proposed model is composed of the premises designed by CFSs, the consequences designed by Takagi-Sugeno linear functions, and a fuzzy causality layer connecting the premises toward the consequences, and it is able to perform prediction of multiple targets. The usage of fuzzy causality in the proposed model makes difference to traditional fuzzy models using If-Then rules, and gives the freedom and flexibility for model construction. To optimize the proposed CNFS, we present a hybrid learning scheme using the particle swarm optimization with multiple swarms (denoted as PSOmsw) and the Kalman filtering algorithm (denoted as KFA). In the hybrid learning method, the KFA updates the consequence parameters while the PSOmsw evolves the rest parameters of the model. The proposed approach has been tested with experiments using several real-world stock market datasets. Compared with other methods, the proposed approach has shown excellent performance. (C) 2019 Published by Elsevier B.V.
机译：在本文中，我们提出了一种新的复杂神经模糊方法来多级预测。使用复杂的模糊集（CFSS），模糊因果区和多群机学习提出了复杂的神经模糊系统（CNFS）。通常，CFSS被视为高级模糊集，其中包含在复杂平面的单位盘中定义的会员度度，与实际值单位间隔中具有隶属度的常规模糊集相反[0,1]。该建议的模型由CFSS设计的房屋组成，由Takagi-Sugeno线性函数设计的后果，以及将场所连接到后果的模糊因果关系层，并且能够执行多个目标的预测。在拟议模型中使用模糊因果关系的使用使得传统模糊模型的差异使用if-then规则，并为模型构造提供自由和灵活性。为了优化所提出的CNFS，我们使用多个群（表示为PSOMSW）和Kalman滤波算法（表示为KFA）的粒子群优化的混合学习方案。在混合学习方法中，KFA更新后果参数，而PSMSW推出模型的其余参数。使用几个现实世界股票市场数据集进行了实验已经测试了所提出的方法。与其他方法相比，所提出的方法表现出出色的性能。 （c）2019年由elestvier b.v发布。

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