Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multilayer perceptron neural networks (MLP-NN). In fact,...

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. Howeve...

In this paper, statistical, artificial neural networks and fuzzy based feature evaluation indices are analysed in order to determine the importance of prostate cancer prognostic markers. Seven prognostic markers are assessed in terms of 3 output classes using logistic regression as a statistical method, multilayer feedforward back propagation neural networks (MLFFBPNN) as a neural network tool,...

Neural networks (NN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand, this flexibility can cause overfitting and can hamper the generalization of neural networks. Many approaches to regularizing NN have been suggested but most of them are based on ad hoc arguments. Employing the principle of transformati...

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neur...

A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator ...

In this paper, we propose an approach for detection of anomalies present in medical images. The idea is to combine tow metaphors: Neural Networks (NN) and Evolutionary Algorithm (EA) in a hybrid system. The Radial Basis Function Neural Network (RBF NN) and Multi Population Genetic Algorithm (MPGA) are coupled in one system called neural-evolutionary algorithm. After applying the growing region ...

This paper presents a speed estimation method using neural networks (NN) in a vector controlled (VC) induction motor drive. The estimation algorithm is implemented using a Jordan recurrent NN structure where training of the NN is done online using back-propagation algorithm. Two back emf models are used in order to realize the reference and the adaptive models from which depending upon the spee...