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.
- SourceAvailable from: unimelb.edu.au[Show abstract] [Hide abstract]
ABSTRACT: A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however, this method produces continuous models with continuous derivatives. It has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables. In addition, the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions.The Annals of Statistics 01/1991; · 2.53 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Wavelet networks are a class of neural networks consisting of wavelets. In this paper, algorithms for wavelet network construction are proposed for the purpose of nonparametric regression estimation. Particular attentions are paid to sparse training data so that problems of large dimension can be better handled. A numerical example on nonlinear system identification is presented for illustrationIEEE Transactions on Neural Networks 04/1997; · 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