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Mathematical Problems in Engineering
Volume 2015, Article ID 623720, 18 pages
http://dx.doi.org/10.1155/2015/623720
Research Article

Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

1Institute of Systems Engineering, Faculty of Science, Jiangsu University, 301 Xuefu, Zhenjiang 212013, China
2Department of Computer Science, Faculty of Applied Science, Kumasi Polytechnic, P.O. Box 854, Kumasi, Ghana
3Computer Science and Technology, Suqian college, Jiangsu University, 399 South Huanghe, 223800, China
4Department of Mathematics and Statistics, School of Applied Science, Kumasi Polytechnic, P.O. Box 854, Kumasi, Ghana
5College of Finance and Economics, Jiangsu University, 301 Xuefu, Zhenjiang 212013, China

Received 14 July 2014; Revised 7 November 2014; Accepted 8 November 2014

Academic Editor: Valder Steffen Jr.

Copyright © 2015 Mary Opokua Ansong 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.

Abstract

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models.