论文信息 - A modified probabilistic neural network signal processor for nonlinear signals

A modified probabilistic neural network signal processor for nonlinear signals

This paper introduces a practical and very effective network for nonlinear signal processing called the modified probabilistic neural network. It is a regression technique which uses a single radial basis function kernel whose bandwidth is related to the noise statistics. It has special advantages in application to time and spatial series signal processing problems because it is constructed directly and simply for the training signal waveform features. A sonar signal processing problem is used to illustrate its operation and to compare it with some other filters and neural networks.

Anthony Zaknich | Yianni Attikiouzel | A. Zaknich | Y. Attikiouzel

[1] E. Nadaraya. On Estimating Regression , 1964 .

[2] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.

[3] D. Broomhead,et al. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[4] W. T. Miller,et al. CMAC: an associative neural network alternative to backpropagation , 1990, Proc. IEEE.

[5] G. S. Watson,et al. Smooth regression analysis , 1964 .

[6] Anthony Zaknich,et al. Application of Artificial Neural Networks to Nonlinear Signal Processing , 1995 .

[7] Anthony Zaknich,et al. The modified probabilistic neural network as a nonlinear correlator detector , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8] Anthony Zaknich,et al. Time Series Characterisation Schemes for the Modified Probabilistic Neural Network , 1995 .

[9] Anthony Zaknich,et al. Application of the Modified Probabilistic Neural Network to the Enhancement of Noisy Short Wave Radio Time and Morse Code Signals , 1995 .

[10] Donald F. Specht,et al. Probabilistic neural networks , 1990, Neural Networks.