Assessing the Noise Immunity and Generalization of Radial Basis Function Networks
Neural Processing Letters
Neural network based optimal control of a biosynthesis process
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
IEEE Transactions on Neural Networks
Bayesian radial basis function neural network
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Neural Acceleration for General-Purpose Approximate Programs
MICRO-45 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture
Neural Processing Letters
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Neural networks are being increasingly used for problems involving function approximation. However, a key limitation of neural methods is the lack of a measure of how much confidence can be placed in output estimates. In the last few years many authors have addressed this shortcoming from various angles, focusing primarily on predicting output bounds as a function of the trained network's characteristics, typically as defined by the Hessian matrix. In this paper the problem of the effect of errors or noise in the presented input vector is examined, and a method based on perturbation analysis of determining output bounds from the error in the input vector and the imperfections in the weight values after training is also presented and demonstrated