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The Forecasting Approach of Short-term Traffic Volume based on Hierarchical Genetic Algorithm and ANFIS

机译:基于层次遗传算法和ANFIS的短时交通量预测方法

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摘要

Adaptive Neural-Fuzzy Inference System(ANFIS) is a perfect method for nonlinear mapping problem which combines neural network and fuzzy inference system.But it only optimizes the internal parameters of the network,not the global structure parameters.In order to solve the problem,an approach to optimize the global structure parameters of ANFIS used by Hierarchical Genetic Algorithm (HGA) is presented.In HGA,every chromosome is coded by hierarchical binary.The genes in chromosome include FIS population,input variables and bit strings of membership function.The demonstration of short-term traffic volume forecasting proves that it can optimize global structure parameters and the results are satisfactory.
机译:自适应神经模糊推理系统(ANFIS)是一种将神经网络和模糊推理系统相结合的非线性映射问题的理想方法,但是它仅优化了网络的内部参数,而不是全局结构参数。为了解决该问题,提出了一种优化遗传算法(HGA)使用的ANFIS全局结构参数的方法。在HGA中,每条染色体均采用分级二进制编码。染色体中的基因包括FIS种群,输入变量和隶属函数位串。短期交通量预测的论证证明,该方法可以优化全局结构参数,效果令人满意。

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