Weighed Nonlinear Hybrid Neural Networks in Underground Rescue Mission

Hongxing Yao,Mary Opokua Ansong,Jun Steed Huang

doi:10.1155/2014/864020

Hongxing Yao, Mary Opokua Ansong + Show 1 more

Open Access

https://doi.org/10.1155/2014/864020

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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 that Y(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.

Highlights

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
Particles positions were updated with new value only when the new value is greater than the previous value; 20% of particles of those obtaining lower values were made to mutate for faster convergence and the structure of adaptive mutation Particle swarm optimization (PSO) (AMPSO) with threshold can be found in [1]
First we used the mix of Sigmoid Basis Function (SBF) and CRBF to present several hybrids with different nonlinear weights of cosine and sine functions on compact radial basis function

Summary

Introduction

Sigmoid basis function (SBF) and radial basis function (RBF) are the most commonly used algorithms in neural training. Wireless sensor networks (WSN) gather and process data from the environment and make possible applications in the areas of environment monitoring, logistics support, health care, and emergency response systems as well as military operations. Multihop transmission in wireless sensor networks conforms to the underground tunnel structure and provides more scalability for communication system construction in rescue situations. This paper proposes a nonlinear Hybrid Neural Networks using radial and sigmoid transfer functions in underground communication, based on particle swarm optimisation. An alternative to this model is without hybrid, either RBF or SBF. The data-mule is discharged to carry items such as food, water, and equipments to the miners underground and return with underground information to rescue team

Preliminaries

Related Work

The Proposed Hybrid

Particle Swarm Optimization

Results and Discussion

Conclusion