In this paper, forecasting of monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks will be compared with those obtained by Box-Jenkins method.

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

Analysis of a biolgically motivated neural network for character recognition. learning for multi-layer feed-forward neural networks using the conjugate gradient method. of japanese kanji using principal component analysis as a preprocessor to an articial neural network.

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...

The purpose of this study is to evaluate the performance analysis of multilayer feed forward neural networks trained with back propagation algorithm & descent gradient Radial basis function network for the pattern classification of hand written curve script. This analysis has been done for handwritten text of three letters and for the individual English vowels. This analysis in the performance ...

Parkinson’s disease is a worldwide frequent neurodegenerative disorder with increasing incidence. Speech disturbance appears during the progression of the disease. UPDRS is a gold standard tool for diagnostic and follow up of the disease. We aim at estimating the UPDRS score based on biomedical voice recordings. In this paper, we study the hubness phenomenon in context of the UPDRS score estima...

This paper is dedicated to the long-term, or multi-step-ahead, time series prediction problem. We propose a novel method for training feed-forward neural networks, such as multilayer perceptrons, with tapped delay lines. Special batch calculation of derivatives called Forecasted Propagation Through Time and batch modification of the Extended Kalman Filter are introduced. Experiments were carrie...

The aim of this paper is to develop the classification system using Artificial Neural Network for Electroencephalogram (EEG) signals. A good standard traditional method is to use Electroencephalogram for diagnosing patients brain functioning that corresponds to epilepsy and different brain disorders. This research focused on designing new classification techniques for single channel EEG recordi...

This paper proposes a neural controller to guide a nonholonomic mobile robot during trajectory tracking. The structure of the controller used consists of two models that describe the kinematical mobile robot system. These models are modified Elman neural networks (MENN) and feed forward multi-layer perceptron (MLP). The modified Elman neural networks model is trained with two stages; off-line a...

Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between...