Journal ArticleDOI
Improving performance in pulse radar detection using Bayesian regularization for neural network training
TLDR
The Bayesian regularization technique used for training the network for pulse radar detection results in superior performance in terms of signal-to-sidelobe ratio compared to the Backpropagation algorithm.About:
This article is published in Digital Signal Processing.The article was published on 2004-09-01. It has received 35 citations till now. The article focuses on the topics: Probabilistic neural network & Barker code.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River
TL;DR: It is shown that the PSO technique can act as an alternative training algorithm for ANNs and is applied to predict water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station.
Journal ArticleDOI
Artificial intelligence for the prediction of water quality index in groundwater systems
TL;DR: Comparison among the performance of different methods for WQI prediction shows that the minimum generalization ability has been obtained for the Bayesian regularization method and Ensemble averaging method and these methods showed the minimum over-fitting problem compared with that of early stopping method.
Journal ArticleDOI
Prediction of body mass index in mice using dense molecular markers and a regularized neural network.
TL;DR: It is concluded that BRANN may be at least as useful as other methods for high-dimensional genome-enabled prediction, with the advantage of its potential ability of capturing non-linear relationships, which may be useful in the study of quantitative traits under complex gene action.
Proceedings ArticleDOI
Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost
TL;DR: This study lays the foundation for the application of BRBPNN in the analysis of oil-gas drilling cost prediction by comparing with Levenberg-Marquardt Back Propagation, Momentum Back Propaganda, Variable Learning Rate Backpropagation models in terms of prediction precision, convergence rate and generalization ability.
Journal ArticleDOI
Comparison of spatial interpolation methods for estimating heavy metals in sediments of Caspian Sea
TL;DR: The results of spatial distribution modeling of Cd, Cu, Hg, Pb, and Zn show that the maximum concentrations of these contaminants are distributed in the south of Caspian Sea, near the boundary of Azerbaijan and Iran.
References
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Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Proceedings ArticleDOI
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Martin Riedmiller,Heinrich Braun +1 more
TL;DR: A learning algorithm for multilayer feedforward networks, RPROP (resilient propagation), is proposed that performs a local adaptation of the weight-updates according to the behavior of the error function to overcome the inherent disadvantages of pure gradient-descent.
Journal ArticleDOI
Bayesian interpolation
TL;DR: The Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data by examining the posterior probability distribution of regularizing constants and noise levels.
Proceedings ArticleDOI
Gauss-Newton approximation to Bayesian learning
F. Dan Foresee,Martin T. Hagan +1 more
TL;DR: The application of Bayesian regularization to the training of feedforward neural networks is described, using a Gauss-Newton approximation to the Hessian matrix to reduce the computational overhead.
Journal ArticleDOI
Optimum Mismatched Filters for Sidelobe Suppression
Martin H. Ackroyd,F. Ghani +1 more
TL;DR: In this article, the application of least-mean-squares approximate inverse filtering techniques to radar range sidelobe reduction is discussed, and a filter which completely suppresses the range sidelobes of a 13-element Barker sequence is only 0.2 dB worse than a matched filter in noise.