An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

Jian-Da Wu, Chuang Chin Hsu, Hui Chu Chen

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.

Original languageEnglish
Pages (from-to)7809-7817
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number4
DOIs
Publication statusPublished - 2009 May 1

Fingerprint

Fuzzy inference
Expert systems
Railroad cars
Neural networks
Backpropagation
Fuzzy logic
Data acquisition
Engines

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference. / Wu, Jian-Da; Hsu, Chuang Chin; Chen, Hui Chu.

In: Expert Systems with Applications, Vol. 36, No. 4, 01.05.2009, p. 7809-7817.

Research output: Contribution to journalArticle

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