Photovoltaic power generation forecasting is an important task in renewable energy power system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic power generation forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) using a data acqui...

constructed from poor Weibull estimates will produce biased volume estimates. Thus, general applicability of a procedure Artificial neural networks (NN) are becoming a popular estimation tool. Because they require no assumptions about the form of a fitting does not mean that the procedure is always best, as growthfunction, they can free the modeler from reliance on parametric and-yield modelers...

Neural Networks (NN) have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationall...

In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our learning algorithm are introduced. We investi...

The potential of Neural Networks (NN) to provide accurate estimates of nonlinear interactions for wind wave spectra by means of direct mapping is considered. Expanding on a previously reported feasibility study, an Empirical Orthogonal Functions (EOF) based NN for single peaked spectra is shown to be much more accurate than the well known Discrete Interaction Approximation (DIA), at the expense...

The identification of non-linear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for non-linear system identification. An RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions ...

Several researchers have successfully used Neural Networks (NN) to process natural languages. In most of cases, decisions about what topology to use are based on previous understanding of how the NN should attack the problem at hand. This paper presents a method for automatically evolving both topologies and learning parameters for the task of processing sentences in English. Results are presen...

In this paper, we present a ‘smart’ neural control scheme for uncertain non-linear systems using the localized radical basis function (RBF) networks. This scheme is designed such that the current control action can utilize the knowledge that the NN learned from the past control process. Compared with most existing adaptive neural controllers, which are in general very-high-order dynamic control...

This paper is concerned with application of Neural Networks (NNs) to fault analysis for both the realtime applications such as protective relaying of transmission lines and the off-line applications such as postmortem study of fault events recorded with Digital Fault Recorders (DFRs). A supervised learning NN of the same type is utilized for both applications. It has been demonstrated that the ...

The use of Neural Networks for skin detection has been reported in many research studies. However, direct use of any color space does not produce optimistic results. This is because the feature vectors to be used as training input to the NN is only size three. This research elaborates on the problem of direct use of color space by analyzing a general images database and show that NN performance...