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Minimum Synthesis and Learning Algorithm for A Hybrid Nonlinear Predictor

混合非线性预测器的最小综合和学习算法

基本信息

批准号：
10650357
负责人：
NAKAYAMA Kenji
金额：
$ 2.11万
依托单位：
Kanazawa University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 1999
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650357/
关键词：
Prediction Nonlinear Neural Networks Linear Prediction Learning Algorithm Time Series 雑音学習

项目摘要

Now a day, we have a lot of problems, environmental disruption, environmental pollution, economic crisis, population problem, natural disaster, nature conservation, and so on. In order to solve these problems, it is very important to analyze progress of these phenomena. These phenomena can be regarded as time series. Mainly they are nonlinear time series. So, nonlinear prediction becomes very important.(1) A Nonlinear PredictorIn this research project, we have developed a hybrid nonlinear predictor, which combines a neural network and a feed-forward linear predictor. Since the neural network has linear output unit, most of nonlinear part and some linear part can be predicted by the neural network. The remaining part is predicted by the linear predictor.(2) Learning AlgorithmsAn improved learning algorithm has been proposed, which separately optimize the neural network and the linear predictor in this order. An enhanced learning algorithm has been proposed for noisy nonlinear time series prediction.(3) Nonlinearity Analysis of Time SeriesPrediction is the mapping from the past sample x(n-1)=[x(n-1),x(n-2),..,x(n-N)] to the next sample x(n). When the past samples x(nィイD21ィエD2-1) and x(nィイD22ィエD2-1) are similar, however, the next samples x(nィイD21ィエD2) and x(nィイD22ィエD2) are far from to each other, then, nonlinearity of this time series is high. A measure, which can evaluate this property has been introduced.(4) Prediction of Real Nonlinear Time SeriesThe proposed method was applied to many the real nonlinear time series, including Chaos, water levels of some lake, fog generation, and so on. The proposed hybrid nonlinear predictor demonstrated good performance compared with the conventional methods.

当今世界，我们面临着许多问题，环境破坏、环境污染、经济危机、人口问题、自然灾害、自然保护等，为了解决这些问题，分析这些现象的进展是非常重要的。这些现象可以看作是时间序列。它们主要是非线性时间序列。因此，非线性预测变得非常重要。(1)一个非线性预测器在这个研究计画中，我们发展了一个混合非线性预测器，它结合了一个神经网路和一个前馈线性预测器。由于神经网络具有线性输出单元，因此大部分非线性部分和部分线性部分可以由神经网络预测。剩余部分由线性预测器预测。(2)学习算法提出了一种改进的学习算法，该算法依次对神经网络和线性预测器分别进行优化。提出了一种用于含噪非线性时间序列预测的增强学习算法。(3)时间序列的非线性分析预测是过去样本x（n-1）=[x（n-1），x（n-2），.，x（n-N）]到下一个样本x（n）。然而，当过去的样本x（n次采样D21次采样D2-1）和x（n次采样D22次采样D2-1）相似时，接下来的样本x（n次采样D21次采样D2）和x（n次采样D22次采样D2）彼此远离，则该时间序列的非线性高。介绍了一种评价这种性质的方法。(4)真实的非线性时间序列的预测将该方法应用于混沌、湖泊水位、雾的产生等真实的非线性时间序列，与传统的预测方法进行了比较，结果表明该方法具有良好的预测性能。