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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >AN AGENT-BASED APPROACH TO IDENTIFICATION OF PREDICTION MODELS
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AN AGENT-BASED APPROACH TO IDENTIFICATION OF PREDICTION MODELS

机译：基于Agent的预测模型识别方法

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摘要

This paper presents an agent-based approach to identification of prediction models in two-dimensional data spaces. A number of agents are sent to the two-dimensional data space that people want to investigate. At the micro-level, every agent tries to build a local linear model by competing with others, and then at the macro-level all surviving agents build the global model by cooperating with each other. And a genetic algorithm is introduced for improving the global model built by the agents. Two examples that apply this approach are given. The advantages of this approach are it does not need people to give a certain formula in advance; and most of time, it can give more precise prediction models than those given by traditional methods.
机译：本文提出了一种基于代理的方法来识别二维数据空间中的预测模型。许多代理被发送到人们要调查的二维数据空间。在微观级别，每个代理都试图通过与其他代理竞争来构建局部线性模型，然后在宏观级别上，所有尚存的代理都通过相互合作来构建全局模型。并引入遗传算法来改进智能体建立的全局模型。给出了应用此方法的两个示例。这种方法的优点是不需要人们预先给出公式。而且在大多数情况下，与传统方法相比，它可以提供更精确的预测模型。

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