Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function ...

Advantages and disadvantages of minimaxing versus product propagation back-up rules for game tree searching have been intensively discussed in the literature. So far, examinations have almost exclusively been carried out through experiments, demonstrating slight superiorities for one or the other back-up rule. In contrast to these purely quantitative investigations, we aim at elaborating differ...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

Back propagation neural network is successfully used in various fields, particularly in pattern recognition. Despite numerous applications, back propagation neural network`s design and optimization are developed by trial-and-error process, which is time-consuming. On the other hand, although a dataset may contain many features, these features may not be useful in a back propagation neural netwo...

This paper presents a biologically plausible mechanism of back-propagating network output error to previous layers of processing in a particular multi-layer neural network. This mechanism is used in a network that is designed to mimic familiarity discrimination as performed by the perirhinal cortex of the temporal lobe. In the algorithm, the error of the network during an initial classification...

Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error backpropagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effect...

To efficiently learn from feedback, the cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error back-propagation. It has been successfully used in both machine learning and modelling of the brain’s cognitive functions. However, in the back-propagation algorithm, ...