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首页> 外文会议>Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232 >A Soft Computing Based Approach for Modeling of Chaotic Time Series
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A Soft Computing Based Approach for Modeling of Chaotic Time Series

机译：基于软计算的混沌时间序列建模方法

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Nonlinear dynamic time series modeling is a generic problem, which permeates all fields of science. The authors have developed a soft computing based methodology for the modeling of systems represented by such series. The soft computing techniques that are a consortium of emerging technologies, have recently provided an alternative approach to mathematical modeling. The implementation of soft computing is based on the exploitation of the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution. Fuzzy logic, neural networks and genetic algorithms are considered to be principal constituents of soft computing. Of these, the first component is primarily concerned with imprecision of data and information, the second with learning, and the third with optimization. In many applications, it is advantageous to exploit the synergism of these methods by using them in combination, rather than alone. The proposed model is based on Kasabov's Evolving Fuzzy Neural Network and employs a genetic algorithm based method for the optimization of the most important parameters that govern the development of its structure. The well-examined Box Jenkins problem, to predict future values of the time series, based on the past history, is used as an illustrative example to demonstrate the potential of the proposed Genetic Evolving Fuzzy Neural Network (GEFuNN) model. The proposed methodology may find applications in the areas of signal processing, control, weather forecasting, economic and business planning and several other fields.
机译：非线性动态时间序列建模是一个普遍的问题，遍及科学的所有领域。作者已经开发了一种基于软计算的方法来对此类系列所代表的系统进行建模。作为新兴技术联盟的软计算技术最近为数学建模提供了另一种方法。软计算的实现基于对不精确性，不确定性和部分事实的容忍度的利用，以实现可处理性，鲁棒性和低成本解决方案。模糊逻辑，神经网络和遗传算法被认为是软计算的主要组成部分。其中，第一个组件主要与数据和信息的不准确性有关，第二个与学习有关，而第三个与优化有关。在许多应用中，通过组合而不是单独使用这些方法来发挥这些方法的协同作用是有利的。提出的模型基于Kasabov的演化模糊神经网络，并采用基于遗传算法的方法来优化控制其结构发展的最重要参数。经过仔细研究的Box Jenkins问题，根据过去的历史预测时间序列的未来值，被用作说明性示例，以证明所提出的遗传进化模糊神经网络（GEFuNN）模型的潜力。所提出的方法可以在信号处理，控制，天气预报，经济和商业计划以及其他几个领域中找到应用。

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