Catálogo de publicaciones - libros

In this paper, a Multilayer Perceptron (MLP) is proposed as a radar detector of known targets in Weibull-distributed clutter. The MLP is trained in a supervised way using the Levenberg-Marquardt backpropagation algorithm to minimize the Mean Square Error, which is able to approximate the Neyman-Pearson detector. Due to the impossibility to find analytical expressions of the optimum detector for this kind of clutter, a suboptimum detector is taken as reference, the Target Sequence Known A Priori (TSKAP) detector. Several sizes of MLP are considered, where even MLPs with very low sizes are able to outperform the TSKAP detector. On the other hand, a sensitivity study with respect to target parameters, as its doppler frequency, is made for different clutter conditions. This study reveals that both detectors work better for high values of target doppler frequency and one-lag correlation coefficient of the clutter. But the most important conclusion is that, for all the cases of the study, the MLP-based detector outperforms the TSKAP one. Moreover, the performance improvement achieved by the MLP-based detector is higher for lower probabilities of false alarm than for higher ones.

This work describes a complete indoor location system, from its creation, development and deployment. This location system is a capable way of retrieving the position of wireless devices using a simple software solution, no additional hardware is necessary. The positioning engine uses artificial neural networks (ANN) to describe the behaviour of a specific indoor propagation channel. The training of the ANN is assured using a slight variation of the radio frequency fingerprinting technique. Results show that the location system has high accuracy with an average error below two meters.

The accurate calculation of the resonance frequency and input resistance of microstrip antennas is a key factor to guarantee their correct behavior. In this paper we presented an adaptive neuro-fuzzy inference system (ANFIS) that calculates resonant frequency and input impedance of the microstrip dipole antenna’s (MSDAs). Although the MSDAs’ resonant frequency greatly depends on the dipole’s length, it also depends on the dipole’s width, the antenna substrate’s permittivity value, and its size (which affects resonant frequency). Input impedance, like resonant frequency, changes with these parameters. According to test results accuracy of ANFIS is calculated 98.91% for resonant frequency while 95.81% for input resistance calculation.

A procedure to estimate the parameters of GARCH processes with non-parametric innovations is proposed. We also design an improved technique to estimate the density of heavy-tailed distributions with real support from empirical data. The performance of GARCH processes with non-parametric innovations is evaluated in a series of experiments on the daily log-returns of IBM stocks. These experiments demonstrate the capacity of the improved estimator to yield a precise quantification of market risk.

This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.

This work uses artificial neural networks, whose architecture were developed using genetic algorithm to realize the hourly load forecasting based on the monthly total load consumption registered by the Energy Company of Pernambuco (CELPE). The proposed Hybrid Intelligent System – HIS was able to find the trade-off between forecast errors and network complexity. The load forecasting produces the essence to increase and strengthen in the basic grid, moreover study into program and planning of the system operation. The load forecasting quality contributes substantially to indicating more accurate consuming market, and making electrical system planning and operating more efficient. The forecast models developed comprise the period of 45 and 49 days ahead. Comparisons between the four models were achieved by using historical data from 2005.

Many applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. However, one of the main difficulties of such kind of system is the natural presence of multiple sound sources in real environments. This paper proposes a pulsed neural network based system for separation and recognition of multiple sound sources based on the difference on time lag of the different sources. The system uses two microphones, extracting the time difference between the two channels with a chain of coincidence detection pulsed neurons. An unsupervised neural network processes the firing information corresponding to each time lag in order to recognize the type of the sound source. Experimental results show that three simultaneous musical instruments’ sounds could be successfully separated and recognized.

This paper presents a novel system that performs text-independent speaker authentication using new spiking neural network (SNN) architectures. Each speaker is represented by a set of prototype vectors that is trained with standard Hebbian rule and approach. For every speaker there is a separated spiking network that computes normalized similarity scores of MFCC (Mel Frequency Cepstrum Coefficients) features considering speaker and background models. Experiments with the VidTimit dataset show similar performance of the system when compared with a benchmark method based on vector quantization. As the main property, the system enables optimization in terms of performance, speed and energy efficiency. A procedure to create/merge neurons is also presented, which enables adaptive and on-line training in an evolvable way.

We present a biologically-inspired neural model addressing the problem of transformations across frames of reference in a posture imitation task. Our modeling is based on the hypothesis that imitation is mediated by two concurrent transformations selectively sensitive to spatial and anatomical cues. In contrast to classical approaches, we also assume that separate instances of this pair of transformations are responsible for the control of each side of the body. We also devised an experimental paradigm which allowed us to model the interference patterns caused by the interaction between the anatomical on one hand, and the spatial imitative strategy on the other hand. The results from our simulation studies thus provide predictions of real behavioral responses.

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005.