Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X ray images with lung nodules. The results shows the high performances of our approach with sensitivity and specificity reaching almost 95% and 90% respectively, with an accuracy of 92.56%. The new methodology lower considerably the computational demands and increases detection performances.

Rainfall estimation based on the impact of rain on electromagnetic waves is a novel methodology that has had notable advancements during the last few years. Many studies conducted on this topic in the past considered only the electromagnetic waves with frequencies greater than 10 GHz since the rainfall impact on the electromagnetic wave attenuation is reduced at lower frequencies. Over the last few years, some authors have demonstrated that there can be a non-negligible attenuation even on the signals received on a global system for mobile communications mobile terminal in presence of rain. In this paper, we propose a new classification method based on a probabilistic neural network to obtain an accurate classification between four rainfall intensities (no rain, weak rain, moderate rain, and heavy rain). The innovative rainfall classification method is based on three received signal level (RSL) local features of the 4G/LTE: the instantaneous RSL, the average RSL value, and its variance calculated by using a sliding window. The proposed method exhibits good performance, obtaining an overall correct classification rate of 96.7%. Almost all papers on this topic present in the literature focus on electromagnetic waves with frequencies greater than 10 GHz, in which the rain impact is more relevant, according to the rain attenuation model. However, only the 4G/LTE signal has such widespread geographic coverage, so the proposed classification method can provide noticeable improvements in the creation of rainfall maps with higher spatial resolution.

This paper presents the main experiences and results obtained about the problem of the lead-acid battery modeling and simulation. A nonlinear mathematical model is presented as well as results of neuroprocessing of the charge-discharge experimental and simulated data. Recurrent neural networks were used to provide a state-of-charge observer and model parameter estimation and tuning. The simulation results are compared with those obtained by extensive lab tests performed on different batteries used for electric vehicle and photovoltaic application.