Weighed Nonlinear Hybrid Neural Networks in Underground Rescue Mission. (22nd January 2014)

• Record Type: Journal Article
• Authors: Yao, Hongxing; Ansong, Mary Opokua; Huang, Jun Steed
• Other Names: Castillo O. Academic Editor.; Chau K. W. Academic Editor.; Chen D. Academic Editor.; Kokol P. Academic Editor.
• Is Part Of: ISRN artificial intelligence. Volume 2014(2014)
• Publication Date: 2014-01-22
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; 006.3
• DOI: 10.1155/2014/864020
• Languages: English
• ISSNs: 2090-7435

Abstract

Abstract : 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 thatY ( out ) = logsig ( R ) + [ exp ⁡ ⁡ ( - abs ( R ) ) ] * ( ± cos ⁡ or ± sin ( R ) ) . 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 (HSCR- BF - cos ), positive cosine (HSCR-BF+cos ), negative sine (HSCR- BF - sin ), 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.