Weighed Nonlinear Hybrid Neural Networks in Underground Rescue Mission

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. 1. Introduction 1.1. Background In our earlier work we demonstrated how a routing path was generated and how the compact radial basis function could be improved by reducing the computational burden of Gaussian by removing the power parameter from the model. We had discussed the robustness and fault tolerant nature of the compact radial basis function for an emergency underground rescue operation and had discussed the performance of the sigmoid basis function and the compact radial basis function of which the latter optimised its parameters better than that of the former [1]. In this paper we look at the hybrid form of this novel algorithm, by introducing nonlinear weights of positive and negative cosine and sine. 1.2. Sigmoid Basis Function (SBF) and Radial Basis Function (RBF) Sigmoid basis function (SBF) and radial basis function (RBF) are the most commonly used algorithms in neural training. The output of the network is a linear combination of radial basis function of the inputs and neural parameters. Radial basis function networks have many uses, including function approximation, time series prediction [2, 3] classification, and system control. The structure supports the academic school of connectionist and the idea was first formulated in 1988 by Broomhead and Lowe [4]. The SBF, a mathematical function having an “S” shape