This paper presents a parameter modeling to Power System Stabilizers (PSS) using MLP and RBF neural networks. The application of neural networks in PSS aims to improve the dynamic stability of electric power systems by reducing the machine eletromechanic damping oscillation when a disturbance occurs. According to the current plant operating conditions, the PSS parameters are automatically adjus...

In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP....

In this paper a Neural Networks (NN) is proposed for transient stability prediction. Transient stability of a power system is first determined based on the generator relative rotor angles procured from time domain simulation outputs. Simulations were carried out on a single machine infinite bus system by considering three phase short circuit fault on the system. The data collected from the time...

This paper presents a new approach to the Artificial Neural Networks (ANN) modelling of bacterial growth; using Neural Network models based on Product Units (PUNN) instead of on sigmoidal units (MLP) of kinetic parameters (lag-time, growth rate and maximum population density) of Leuconostoc mesenteroides and those factors affecting their growth such as storage temperature, pH, NaCl and NaNO 2 c...

Several recent works have used neural networks to discriminate vigilance states in humans from electroencephalographic (EEG) signals. Our study aims at being more exhaustive. It takes into account various connectionist models, and it precisely studies their features and their performances. Physicians have been associated to the project, especially when tuning our models. Above all, our work has...

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve comple...

Neural networks (NNs) have been increasingly used in recent years for the solving complex nonlinear problems. NNs are seen as an attractive alternative to process based modeling approaches, as they are able to extract an underlying relationship from the data when knowledge of physical process is lacking. The paper evaluates the predictive power of a model, which emulates an army commander on th...

It is easy for a multi-layered perception (MLP) to form open plane classification borders, and for a radial basis function network (RBFN) to form closed circular or elliptic classification borders. In contrast, it is difficult for a MLP to form closed circular or elliptic classification borders, and for RBFN to form open plane classification borders. Hence, MLP and RBFN have their own advantage...

We present a study to optimize multi-layer perceptron (MLP) classification power with a Rocks Linux cluster [1]. Simulated data from a future high energy physics experiment at the Large Hadron Collider (LHC) is used to teach a neural network to separate the Higgs particle signal from a dominant background [2]. The MLP classifiers have been implemented using the ROOT data analysis framework [3]....

In this job, short-term forecasts are calculated for the Energy Price in the Electricity Production Market of Spain. The methodology used to achieve these forecasts is based on Artificial Neural Networks, which have been used succesfully in recent years in many forecasting applications. To gauge the quality of forecasts, they have been compared with those obtained with the Box-Jenkins ARIMA mod...