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, ...

objective: in this study, artificial neural network (ann) analysis of virotherapy in preclinical breast cancer was investigated. materials and methods: in this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. the input parameters of the model were virus dose, week and tamoxifen ci...

suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. there are different methods for suspended sediment estimation. this study aims to estimate suspended sediment using feed forward neural network with error back propagation with levenberg-marquardt back propagation algo...

In this paper we propose a new learning algorithm for locally recurrent neural networks, called Truncated Recursive Back Propagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. for TDNN, and includes the Back and Tsoi algorithm as well as BPS and standard on-line Back Propagation as particular cases. The proposed...

This paper presents the use of ANN as a pattern classifier for differential protection of power transformer, which makes the discrimination among normal, magnetizing inrush, over-excitation, external fault and internal fault currents. The Back Propagation Neural Network Algorithm and Genetic Algorithm are used to train the multi-layered feed forward neural network and simulated results are comp...

An algorithm that is faster than back-propagation and for which it is not necessary to specify the number of hidden units in advance is described. The relationship with other fast pattern-recognition algorithms, such as algorithms based on k-d trees, is discussed. The algorithm has been implemented and tested on artificial problems, such as the parity problem, and on real problems arising in sp...

Currently, the back-propagation is the most widely applied neural network algorithm at present. However, its slow learning speed and local minima problem are often cited as the major weakness of the algorithm. In this paper, described are an adaptive training algorithm based on selective retraining of patterns through error analysis, and dynamic adaptation of learning rate and momentum through ...