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ISRN Artificial Intelligence
Volume 2014 (2014), Article ID 864020, 13 pages
Research Article

Weighed Nonlinear Hybrid Neural Networks in Underground Rescue Mission

1Institute of System Engineering, Faculty of Science, Jiangsu University, 301 Xuefu, Zhenjiang 212013, China
2College of Finance and Economics, Jiangsu University, 301 Xuefu, Zhenjiang 212013, China
3Department of Computer Science, School of Applied Science, Kumasi Polytechnic, P.O. Box 854, Kumasi, Ghana
4Computer Science and Technology, School of Computer Science & Telecommunication, Jiangsu University, 301 Xuefu, Zhenjiang 212013, China

Received 25 September 2013; Accepted 11 November 2013; Published 22 January 2014

Academic Editors: O. Castillo, K. W. Chau, D. Chen, and P. Kokol

Copyright © 2014 Hongxing Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In our previous work, a novel model called compact radial basis function (CRBF) in a routing topology control has been modelled. The computational burden of Zhang and Gaussian transfer functions was modified by removing the power parameters on the models. The results showed outstanding performance over the Zhang and Gaussian models. This study researched on several hybrids forms of the model where cosine (cos) and sine (sin) nonlinear weights were imposed on the two transfer functions such that . The purpose was to identify the best hybrid that optimized all of its parameters with a minimum error. The results of the nonlinear weighted hybrids were compared with a hybrid of Gaussian model. Simulation revealed that the negative nonlinear weights hybrids optimized all the parameters and it is substantially superior to the previous approaches presented in the literature, with minimized errors of 0.0098, 0.0121, 0.0135, and 0.0129 for the negative cosine (), positive cosine (HSCR-BF+cos), negative sine (), and positive sine (HSCR-BF+sin) hybrids, respectively, while sigmoid and Gaussian radial basis functions (HSGR-BF+cos) were 0.0117. The proposed hybrid could serve as an alternative approach to underground rescue operation.