Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment

Gaige Wang,Hong Duan,Lihong Guo

doi:10.1155/2013/632437

Abstract

Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is 1.23 × 10−3, which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment.

Highlights

With the development of science and technology, the requirement of information is increasingly improving in modern warfare
For Wavelet neural network (WNN), one of the biggest drawbacks is the difficulty of the choice of mother wavelet function, so this paper proposes an algorithm for selecting optimal mother wavelet function
The results show that the proposed network is superior to the WNN, particle swarm optimization (PSO) support vector machine (SVM) and BP neural network

Summary

Introduction

With the development of science and technology, the requirement of information is increasingly improving in modern warfare. The traditional methods to solve threat assessment are Bayesian inference [1, 2], multiattribute decision-making theory [3], GSOBP [4], Elman AdaBoost [5], analytic hierarchy process [6], Dempster-Shafer theory [7], Hypothesis-drive [8], and so forth. As the neural network has many advantages, such as strong learning ability and adaptability, it is adept at working out the target threat assessment compared with the above-mentioned methods. We construct the (Multiple Wavelet Function Wavelet Neural Networks) MWFWNN using the above method to select optimal mother wavelet function for threat assessment

MWFWNN Network

MWFWNN Algorithm

Target Threat Assessment Using MWFWNN

Model Simulation

Conclusion