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Colin F. N. Cowan
Researcher at University of Edinburgh
Publications - 50
Citations - 5126
Colin F. N. Cowan is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Adaptive filter & Recursive least squares filter. The author has an hindex of 21, co-authored 50 publications receiving 4971 citations.
Papers
More filters
Journal ArticleDOI
Orthogonal least squares learning algorithm for radial basis function networks
TL;DR: The authors propose an alternative learning procedure based on the orthogonal least-squares method, which provides a simple and efficient means for fitting radial basis function networks.
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Practical identification of NARMAX models using radial basis functions
TL;DR: In this paper, an algorithm for identifying NARMAX models based on radial basis functions from noise-corrupted data is presented. But this algorithm is not suitable for the analysis of a wide class of discrete-time nonlinear systems.
Journal ArticleDOI
Adaptive equalization of finite non-linear channels using multilayer perceptions
TL;DR: It is shown that difficulties associated with channel non-linearities and additive noise correlation can be overcome by the use of equalizers employing a multi-layer perceptron structure, providing further evidence that the neural network approach proposed recently by Gibson et al. is a general solution to the problem of equalization in digital communications systems.
Journal ArticleDOI
Parallel recursive prediction error algorithm for training layered neural networks
TL;DR: A new recursive prediction error algorithm is derived for the training of feedforward layered neural networks that enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence properties than the classical back propagation algorithm.
Journal ArticleDOI
Non-linear systems identification using radial basis functions
TL;DR: In this article, a forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial basis function centers from a large number of possible candidates.