Paper Titles

Preface and Committees

Optimization Design, Modeling and Thermal Stress for the 12t Reduction and Distillation Container of Sponge Titanium
p.3

Supplier Score Prediction Using Hybrid Neural Network Model Based on Simple Exponential Smoothing
p.9

Optimization of Electro-Discharge Diamond Surface Grinding Process Parameters with Multiple Performance Characteristics of Ti-6Al-4V Using Grey-Taguchi Approach
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Multi Response Optimization of Electrical Discharge Machining Process Parameters Using Sintered Copper Electrode
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FE-Based Analysis of Heat Bending of Thin-Walled Ti-Alloy Tube with Large Diameter and Small Bending Radius
p.25

Optimization of Process Parameters in Micro Electro- Discharge Drilling [micro EDM-Drilling]: A Taguchi Approach
p.30

Multi Objective Optimization in Scheduling of FMS Using Roulette Wheel Selection Process
p.35

Hybridized Genetic Algorithms in the Optimization of a PIFA Antenna Using Fitness Characterization and Clustering
p.40

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 622-623Supplier Score Prediction Using Hybrid Neural...

Supplier Score Prediction Using Hybrid Neural Network Model Based on Simple Exponential Smoothing

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Abstract:

This paper proposes a hybrid model combining artificial neural networks (ANN) and simple average exponential smoothing (SES) forecasting models, termed as the ANNSES model. The proposed model attempts to incorporate the linear characteristics of SES and nonlinear patterns of ANN for predicting the score of suppliers in an e-procurement system of an automobile industry. The MAPE and RMSE errors obtained indicate that predictions upto a month ahead was accurate using the hybrid model compared to those obtained using ANN and SES forecasting models individually.

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Manufacturing Science and Technology III

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Periodical:

Advanced Materials Research (Volumes 622-623)

Pages:

9-13

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.622-623.9

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Online since:

December 2012

Authors:

P. Priya, K. Iyakutti, S. Prasanna Devi

Keywords:

Artificial Neural Network (ANN), Hybrid ANNSES, SES, Supplier Selection

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Priya P., Iyakutti K., Prasanna Devi S., Web questionnaire validation and vendor selection using Adaptive Neuro Fuzzy Inference System, Second international conference on sustainable energy and intelligent system (SEISCON 2011), Chennai, India, pp: 805-808, indexed in IEEE Xplore.

DOI: 10.1049/cp.2011.0476

[2] Priya P., Iyakutti K., Prasanna Devi S., "e-procurement system with embedded supplier selection DSS for an automobile manufacturing industry, Vol 4, issue 4, 2012, (accepted for publications and listed in forthcoming papers): http: /airccse. org/journal/ijdms/jdms_forth. html.

DOI: 10.5121/ijdms.2012.4206

[3] Peter Zhang G., Time series forecasting using a hybrid ARIMA and neural network model, Neuro computing, vol. 50, pp.159-175, (2003).

DOI: 10.1016/s0925-2312(01)00702-0

[4] Chaouachi A., R.M. Kamel, R. Ichikawa, H. Hayashi, and K. Nagasaka, Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting, International Journal of Information and Mathematical Sciences, vol. 5, pp.332-337, (2009).

[5] Wedding D.K., K.J. Cios, Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing, vol. 10, pp.149-168, (1996).

DOI: 10.1016/0925-2312(95)00021-6

[6] Areekul, P., Senjyu, T., Toyama, H., Yona, A., A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market, IEEE Transactions on Power Systems, vol. 25, pp.524-530, (2010).

DOI: 10.1109/tpwrs.2009.2036488

[7] Durdu O¨mer Faruk, A hybrid neural network and ARIMA model for water quality time series prediction, Engineering Applications of Artificial Intelligence, vol. 23, pp.586-594, (2010).

DOI: 10.1016/j.engappai.2009.09.015

[8] Mehdi Khashei, Seyed Reza Hejazi, Mehdi Bijari, A new hybrid artificial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems, vol. 159, pp.769-786, (2008).

DOI: 10.1016/j.fss.2007.10.011

[9] Dong C.J. and Z.Y. Liu, Multi-layer neural network involving chaos neurons and its application to traffic-flow prediction Journal of System Simualtion, vol. 19, pp.4450-4453, (2007).

[10] Gu Y.L., C.F. Shao, and X.Y. Tan, Dynamic traffic volume forecast method based on chaotic neural network, Traffic & Computer, vol. 25, pp.28-30, (2007).

[11] Ying Chen, Luh, P.B., Che Guan, Yige Zhao, Michel, L.D., Coolbeth, M.A., Friedland, P.B., Rourke, S.J., Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks, IEEE Transactions on Power Systems, vol. 25, pp.322-330, (2010).

DOI: 10.1109/tpwrs.2009.2030426

[12] X.L. ZHANG, and G.G. HE, Forecasting approach for short-term traffic flow based on principal component analysis and combined neural network, Systems Engineering - Theory & Practice, vol. 27, pp.167-171, (2007).

DOI: 10.1016/s1874-8651(08)60052-6

[13] Song G.J.,. C. Hu, K.Q. Xie, and R. Peng, Process neural network modeling for real time short-term traffic flow prediction, Journal of Traffic and Transportation Engineering, vol. 9, pp.73-77, (2009).

[14] Zarita Zainuddin, Ong Pauline and Cemal Ardil, A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients, International Journal of Information and Mathematical Sciences, vol. 5, pp.72-79, (2009).

[15] Brown, R.G.: Statistical Forecasting for Inventory Control. New York, McGraw-Hill, (1959).