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ISRN Artificial Intelligence
Volume 2014 (2014), Article ID 864020, 13 pages
http://dx.doi.org/10.1155/2014/864020
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.

Supplementary Material

For more information of the generation of the results for the routing path R, as discussed in the preliminaries in section 2, readers are referred to the online supplementary material for details. Each column on the excel worksheet represent one of the highest survival probability for rescue operations, in a form of matrices and vectors representing deployment locations, connection, routing, fault tolerance, etc., before the final survival vector αR is generated. This αR is iterated 6 times (where 6 is the total number of iterations) taking into consideration the rock hardness cases β (Table 2). All the 6 cases are considered in the final survival vectors R. This R in equation (13) is the final survival probability vector which is used as the input to the neural network training as shown in Figure 1.

  1. Supplementary Material