In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a horizontal surface. We use an ad-hoc time series preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar radiation at daily horizon. First results are promising with nRMSE < 21% and RMSE < 998 Wh/m2...

A new objective function for neural net classifier design is presented, which has more free parameters than the classical objective function. An iterative minimization technique for the objective function is derived which requires the solution of multiple sets of numerically ill-conditioned linear equations. An enhanced feedforward network training algorithm is derived, which solves linear equa...

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phas...

In this paper, we present some practical experiments for continuous speech frame-by-frame phoneme classification using Multi Layer Perceptron (MLP) neural networks. We used to train and test our software application, the the OASIS Numbers speech database. In our experiments, we tried to classify all the existing 32 phonemes together, from OASIS Numbers database dictionary. We also used differen...

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks (MLP) for time series prediction. Experimental tests were carried out on four simulated series with known features and on the Sunspot series. The results have shown that this approach selects the appropriate time series lags and builds a...

This paper proposes the application to the liver fibrosis stadialization of a novel training technique of feed-forward neural networks based on the Bayesian paradigm. Using the Pearson’s r correlation coefficient instead of the standard backpropagation algorithm to update the synaptic weights of a multi-layer perceptron, the proposed model is compared with traditional machine learning algorithm...

The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers ...

This paper designs a reconfigurable 5-fingers microstrip patch antenna using artificial neural networks (ANNs). The particular neural networks selected for this task are the source difference method (SD), multilayer perceptron (MLP), prior knowledge input method (PKI) and difference prior knowledge input method (PKID). Employing fine and coarse models for training the networks enables to develo...

In this paper, a closed-loop, smart transducer design is proposed, based on artificial neural network (ANN) techniques. The design aims to improve the performance of open-loop, off-the-shelf capacitive acceleration sensors and increase their robustness to manufacturing tolerances. A Òmodel referenceÓ control strategy was adopted for the design of the smart transducer. Multilayer perceptron (MLP...

An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydrologically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks ...