The backpropagation algorithm is widely used for training multilayer neural networks In this publication the gain of its activation function s is investigated In speci c it is proven that changing the gain of the activation function is equivalent to changing the learning rate and the weights This simpli es the backpropagation learning rule by eliminating one of its parameters The theorem can be...

Although Backpropagation (BP) is commonly used to train Multiple Layer Perceptron (MLP) neural networks and its original algorithm has been significantly improved several times, it still suffers from some drawbacks like being slow, getting stuck in local minima or being bound to constraints regarding the activation (transfer) function of the neurons. This paper presents the substitution of back...

The development of the backpropagation learning rule has been a landmark in neural networks. It provides a computational method for training multilayer networks. Unfortunately, backpropagation suffers from several problems. In this paper, a new technique based upon Genetic Programming (GP) is proposed to overcome some of these problems. We have used GP to discover new supervised learning algori...

and ei(t) is the output error, xi(t) represent the activations and δi(t) are the backpropagated errors. The system described by Eq. 1 and Eq. 2 constitute the backpropagation through time (BPTT) algorithm. Note that the backpropagation system (Eq. 2) should be run from t=T backwards to t=1. We define the boundary conditions δi(T+1)=0. We will assume that the instantaneous error signal ei(t) is ...

In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; PerceptronBackpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test...

Proposes a neural network based invariant character recognition system using double backpropagation network. The model consists of two parts. The first is a preprocessor which is intended to produce a translation, rotation and scale invariant representation of the input pattern. The second is a neural net classifier. The outputs produced by the preprocessor at the first stage are classified by ...

The backpropagation algorithm is widely used for training multilayer neural networks. In this publication the gain of its activation function(s) is investigated. In specific, it is proven that changing the gain of the activation function is equivalent to changing the learning rate and the weights. This simplifies the backpropagation learning rule by eliminating one of its parameters. The theore...

The olfactory receptor neuron provides a good opportunity to analyze a biophysical model of a single neuron because its dendritic structure is simple and even close to a cylinder in the case of the moth sex-pheromone receptor cell. We have considered this cylindrical case and studied two main problems. First, we were concerned with the effect of the neuron's length on the receptor potential for...

Stripe rust caused by Puccinia striiformis f. sp. tritici, is a devastating wheat disease in the world. The prediction of this disease is very important to make control strategies. In order to figure out suitable prediction methods based on neural networks that could provide accurate prediction information with high stability, the predictions of wheat stripe rust by using backpropagation networ...

The Architecture of BPNN’s A population P of objects that are similar but not identical allows P to be partitioned into a set of K groups, or classes, whereby the objects within the same class are more similar and the objects between classes are more dissimilar. The objects have N attributes (called properties or features) that can be measured (observed) so that each object can be represented b...