• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model
【24h】

A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

机译：基于模糊聚类和人工神经网络的单乘神经元模型的混合模糊时间序列方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.
机译：特别是近年来，人工智能优化技术已被用于使模糊时间序列方法更加系统化并提高预测性能。此外，在观测的模糊化和模糊关系的确定中分别采用了一些模糊聚类方法和结构不同的人工神经网络。在考虑隶属度值的方法中，主观地确定隶属度值，或者通过在确定关系时考虑到隶属度值之间存在关系来获得系统的模糊输出。这需要去模糊化步骤并增加模型误差。在这项研究中，通过使用Gustafson-Kessel模糊聚类技术可以更系统地获得隶属度值。人工神经网络与单乘性神经元模型一起用于模糊关系的识别，消除了体系结构选择问题，并且通过根据时间序列的实际观测值构成目标值，消除了去模糊步骤的必要性。利用粒子群算法对单乘法神经元模型进行模糊关系步长的人工神经网络训练。所提出的方法使用各种时间序列来实现，并将结果与​​先前的研究进行比较以证明所提出方法的性能。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|560472.1-560472.9|共9页
  • 作者

    Ozge Cagcag Yolcu;

  • 作者单位

    Department of Statistics, Faculty of Arts and Sciences, Ondokuz Mayis University, 55139 Samsun, Turkey;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号