Hybrid Support Vector Regression and GA/TS for Radio-Wave Path-Loss Prediction

Abstract

AbstractThis 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 path-loss 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.KeywordsSupport vector regressiongenetic algorithms and tabu searchradio-wave path-loss