@article{Bonanno2012956, author = ”F. Bonanno and G. Capizzi and G. Graditi and C. Napoli and G.M. Tina”, title = ”A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module ”, journal = ”Applied Energy ”, volume = ”97”, number = ”0”, pages = ”956 961”, year = ”2012”, issn = ”0306-2619”, doi = ”http://dx.doi.org/10.1016/j.apenerg...

A strategy for detection of feedstock variations in a continuous pulp digester is presented. A Gaussian Radial Basis Function Neural Network is used to infer these unmeasured variations. The absence of plant data motivates the development of training set data. The efficiency and limitation of the approach is demonstrated with a first principles model.

This paper is concerned with the consensus tracking problem of high order MIMO nonlinear multi-agent systems. The agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. The communication network topology of agents is assumed to be a fixed undirected graph. A distributed adaptive control method is proposed to solve the consensus problem utilizing re...

In industry, a comprehensive control process is necessary in order to ensure the quality of a manufactured product. In the manufacturing process of concrete, the variables are dependent on several factors, some of them external, which require very precise estimation. To resolve this problem we use techniques based on artificial neural networks. Throughout this paper we describe an RBF (Radial B...

In this work, some ubiquitous neural networks are applied to model the landscape of a known problem function approximation. The performance of the various neural networks is analyzed and validated via some well-known benchmark problems as target functions, such as Sphere, Rastrigin, and Griewank functions. The experimental results show that among the three neural networks tested, Radial Basis F...

In order to further improve the precision and generalization ability of the neural network based performance model of engine, back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) have been investigated. The topologies and algorithms of these three different types of neural networks have been designed to meet the sa...

The measurement and compensation of volumetric positioning errors can greatly improve the accuracy of the machine tools. In this paper, a sequential step diagonal measurement is introduced to calibrate 9 volumetric positioning errors in a quick way. Measurements under various thermal conditions are preformed to understand the relationship between the volumetric positioning errors and the temper...

Currently, most automobiles have automatic transmission systems. The gear-shifting strategy used to generate shift patterns in transmission systems plays an important role in improving the performance of vehicles. However, conventional transmission systems have a fixed type of shift map, so it may not be enough to provide an efficient gear-shifting pattern to satisfy the demands of driver. In t...

Wind speed forecast is essential in wind energy conversion system and may fail to operate power plant at non optimal region if not properly forecasted. This paper focuses the short term wind speed forecasting using conventional statistical method and artificial neural networks such as back propagation network (BPN), generalized regression neural network (GRNN) and radial basis function networks...