Neural network

A theoretical comparison of neural network (NnM) and thermodynamic (ThM) models is carried out to estimate on-line the coefficient of performance (COP) in an absorption heat transformer integrated with a water purification process (AHT–WP). The NnM has been computed for16 variables measured by senso

A predictive model for a water purification process integrated in an absorption heat transformer, using an artificial neural network, is proposed in order to obtain on-line predictions of the coefficient of performance (COP). This model takes into account the input and output temperatures for each o

In this study we introduce a new idea of utilizing algorithms from the Computational Intelligence community in building accurate models for saline water evaporation rates. Three experimental methods were used to measure the evaporation rate for different brine concentrations, different water and air

In this paper, we investigate using an adaptive radial basis function (RBF) network with infinite impulse response (IIR) filter in order to find a suitable model for sizing coefficients of the stand-alone photovoltaic (PV) systems, based on minimum of input data. These sizing coefficients allow to t

We propose a network architecture based on adaptive receptive fields and a learning algorithm that combines both supervised learning of centers and unsupervised learning of output layer weights. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input spa