New modeling algorithm for improving accuracy of weapon launch acceptability region
ABSTRACT In this paper, we propose a new modeling method for improving accuracy of weapon launch acceptability region. To enhance the model accuracy and memory requirements, the proposed method is based on training technique using wavelet neural network. The accuracy relative to the truth data set, which consists of grid data, generated using the weapon 6 degree of freedom simulation is evaluated. In the experiment results, true/false coverage area statistics show about 3.42% and 2.86% improvement compared to the conventional modeling method. Also, the memory requirements lead to about a 60% reduction on average.
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ABSTRACT: In this paper one approach is proposed for using wavelets in non parametric regression estimation. The proposed non parametric estimator, named wavelet network, has a neural network like structure, but consists of wavelets. It makes use of techniques of regressor selection completed with backpropagation procedures. It is capable of handling nonlinear regressions of moderately large input dimension with sparse training data. Numerical examples are reported to illustrate the performance of this proposed approach.IEEE Transactions on Neural Networks 04/1997; DOI:10.1109/72.557660 · 2.95 Impact Factor
Conference Paper: Function approximation using robust wavelet neural networks[Show abstract] [Hide abstract]
ABSTRACT: Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators but achieve faster convergence and are capable of dealing with the so-called "curse of dimensionality". In addition, WNN are generalized RBFN. However, the generalization performance of WNN trained by least-squares approach deteriorates when outliers are present. In this paper, we propose a robust wavelet neural network based on the theory of robust regression for dealing with outliers in the framework of function approximation. By adaptively adjusting the number of training data involved during training, the efficiency loss in the presence of Gaussian noise is accommodated. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed network.Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on; 02/2002
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