Smart Growth Evaluation System based on Fuzzy Theory and Neural Network

Research Area: Volume 6,Issue 5, Sept. 2017 Year: 2017
Type of Publication: Article Keywords: Smart Growth; Evaluation System; Fuzzy Theory; Neural Network
Authors:
  • Jie Yu
  • Zhangu Wang
Journal: IJEIR Volume: 6
Number: 5 Pages: 199-204
Month: September
ISSN: 2277-5668
Abstract:
This paper puts forward a method of smart growth based on fuzzy theory and neural network. First of all, we have collected a large number of references and expert advice to make a qualitative analysis of the factors that may affect the development of smart city, and get 21 evaluation indicators. Dynamic particle swarm algorithm and fuzzy theory are used to extract and optimize the possible indicators, finally we get twelve of the most sensitive evaluating indicators. The fuzzy transformation would be utilized to complete the quantitative analysis of each evaluating indicator by giving each indicator a corresponding weight. In the course of actual evaluation, we adjust the weight slightly by training BP neural network and output the final evaluation score

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UIF : 1.1094 , ICV = 75.71

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