Regression Analysis for Predicting Wood Pulp Demand by PSO Optimization

V.Anandhi

Regression Analysis for Predicting Wood Pulp Demand by PSO Optimization

V.Anandhi * §

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Tamil Nadu Agricultural University

Regression Analysis for Predicting Wood Pulp Demand by PSO Optimization

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Abstract

In today’s world, consumption of paper and paperbased products is increasing in all the fields. Wood pulp which is extracted from the wood chips is the most commonly used raw material to manufacture the papers. Demand and supply of the wood pulp determines the socialeconomical development of a country. Many forecasting methods are used to predict the future demands of the wood pulp so that the supply chain management can be planned. In this paper, support vector regression analysis methods are used to predict the demands of wood pulp and Particle Swarm Optimization (PSO) algorithm is proposed to optimize the parameters of kernel functions. Regression models were created by using the data collected from TNPL. The parameters such as Mean Magnitude Relative Error (MMRE) and Median Magnitude Relative Error (MdMRE) are used for evaluating the results. Evaluated result shows that proposed SVM regression with PSO approach gave improved accuracy with significant decrease in MMRE and MdMRE.

References

9 Cites in Article

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9. V Anandhi,R Manicka Chezian (2012). Forecast of Demand and Supply of Pulpwood using Artificial Neural Network. International Journal of Computer Science and Telecom-munications, 3

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

V.Anandhi. 2013. "Regression Analysis for Predicting Wood Pulp Demand by PSO Optimization". Global Journal of Science Frontier Research - H: Environment & Environmental geology GJSFR-H Volume 13 (GJSFR Volume 13 Issue H3).

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GJSFR Volume 13 Issue H3

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

demand supply management Particle Swarm Optimization RBF support vector regression analysis wood pulp

Classification

GJSFR-H Classification FOR Code: 860399

Submission ReceivedAugust 13, 2013
Peer Review Double Blind
Handling Editor
Accepted September 3, 2013
Published September 14, 2013

Version of record

v1.2

Issue date

September 18, 2013

Language

English

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Article Matrices

Total Score: 151

Country: India

Subject: Global Journal of Science Frontier Research

Authors: V.Anandhi, Dr. R. Manicka Chezian (PhD/Dr. count: 1)

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Publish Date: 2013 09, Sat

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