NDLI: A price forecast model of hydraulic cylinders through integration of case based reasoning and back-propagation neural networks

Content Provider	IEEE Xplore Digital Library
Author	Min Zhou Yanxia Chen Ting Gao
Copyright Year	2013
Description	Author affiliation: Coll. of Machinery & Autom., Wuhan Univ. of Sci. & Technol., Wuhan, China (Min Zhou; Yanxia Chen; Ting Gao)
Abstract	Various kinds of hydraulic cylinders are widely used and have large variations in price. It has great significance to establish a hydraulic cylinder price forecast model for the issues of making procurement plans and the economical evaluation of maintenance and remanufacturing. However, it is hard to establish a direct mathematical model because there are many factors influencing the hydraulic cylinder price. The objective of this paper is to develop a price forecast model for hydraulic cylinders through the application of case based reasoning (CBR) and back-propagation neural networks (BPNN). Seven critical factors related to the hydraulic cylinder price have been found out by means of the Delphi method. They are cylinder bore diameter, stroke, pressure rating, servo cylinder or not, installation type, supplier qualification, and Producer Price Index (PPI). The PPI introduced in the model can improve the model sensitivity to market change. Case study is used to prove that both the BPNN model and the integrated CBR and BPNN model are capable of forecasting hydraulic cylinder price. The forecast of the integrated CBR and BPNN model is more accuracy than that of BPNN model.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	111
Ending Page	115
File Size	259366
Page Count	5
File Format	PDF
ISBN	9781467347143
ISSN	21579563
DOI	10.1109/ICNC.2013.6817954
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	case based reasoning Estimation Artificial neural networks Predictive models Cognition price forecast BP neural networks Servomotors hydraulic cylinders Qualifications
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The Research on Stock Price Forecast Model Based on Data Mining of BP Neural Networks

Study of Mid and Long-term Runoff Forecast Based on Back-propagation Neural Network

Vegetable price prediction using data mining classification technique

Price Forecast for Gold Futures Based on GA-BP Neural Network

Leakage Forecasting of Karst Reservoir Using Back-Propagation Artificial Neural Network: A Case of Shuibuya Hydroproject

The Unemployment Rate Forecast Model Basing on BP Neural Network

Prediction model of agricultural product's price based on the improved BP neural network

Urbanization level forecast based on BP neural network

A Novel Inventory Level Forecast Model Based on Artificial Neural Network

A price forecast model of hydraulic cylinders through integration of case based reasoning and back-propagation neural networks

Similar Documents

The Research on Stock Price Forecast Model Based on Data Mining of BP Neural Networks

Study of Mid and Long-term Runoff Forecast Based on Back-propagation Neural Network

Vegetable price prediction using data mining classification technique

Price Forecast for Gold Futures Based on GA-BP Neural Network

Leakage Forecasting of Karst Reservoir Using Back-Propagation Artificial Neural Network: A Case of Shuibuya Hydroproject

The Unemployment Rate Forecast Model Basing on BP Neural Network

Prediction model of agricultural product's price based on the improved BP neural network

Urbanization level forecast based on BP neural network

A Novel Inventory Level Forecast Model Based on Artificial Neural Network

A price forecast model of hydraulic cylinders through integration of case based reasoning and back-propagation neural networks