Hybrid support vector regression and GA/TS for radio-wave path-loss prediction

  • Authors:

  • Kuo-Chen Hung;Kuo-Ping Lin;Gino K. Yang;Y.-C. Tsai

  • Affiliations:

  • Department of Logistics Management, National Defense University, Beitou, Taipei, Taiwan;Department of Information Management, Lunghwa University of Science and Technology, Taiwan, R.O.C.;Department of Computer Science and Information Management, Hungkuang University, Taiwan, R.O.C.;Department of Marketing and Distribution Management, Overseas Chinese University, Taiwan, R.O.C.

  • Venue:
  • ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
  • Year:
  • 2010

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Abstract

This paper presents support vector regression with hybrid genetic algorithms and tabu search (GA/TS) algorithms (SVRGA/TS) models for the prediction of radio-wave path-loss in suburban environment. The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the application of SVR model in a radio-wave path-loss forecasting has not been widely investigated. This study aims at developing a SVRGA/TS model to forecast radio-wave pathloss data. Furthermore, the genetic algorithm and tabu search techniques have be applied to select important parameters for SVR model. In this study, four forecasting models, Egli, Walfisch and Bertoni (W&B), generalized regression neural networks (GRNN) and SVRGA/TS models are employed for forecasting the same data sets. Empirical results indicate that the SVRGA/TS outperforms other models in terms of forecasting accuracy. Thus, the SVRGA/TS model is an effective method for radio-wave path-loss forecasting in suburban environment.