Um modelo em simulink para sintonia automática de controladores PID usando redes neuronais

The PID controllers are widely used in industry. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned. During the last years, several methods have been proposed for PID autotuning.

Algoritmos construtivos para treino de redes neuronais b-spline

Dissertação de mest. em Engenharia de Sistemas e Computação - Área de Sistemas de Controlo, Faculdade de Ciências e Tecnologia, Univ.do Algarve, 2001

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Predictive models of buildings energy consumption

Dissertação de Mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

Desenho de modelos neuronais para a deteção de sismos

A performance dos detetores sísmicos atualmente utilizados pode e deve ser melhorada. Atualmente existem vários algoritmos para a deteção de sismos de forma automática, desde os sistemas simples baseados em STA/LTA, aos mais sofisticados baseados em reconhecimento de padrões. Este estudo pretende dar continuidade ao desenvolvimento de uma abordagem de deteção de eventos sísmicos ao nível da estação local, utilizando uma técnica bastante conhecida, chamada Máquina de Vetores de Suporte (SVM). SVM é amplamente utilizada em problemas de classificação, devido a sua boa capacidade de generalização. Nesta experiência, a técnica baseada em SVM é aplicada em diferentes modos de operações. Os resultados mostraram que a técnica proposta dá excelentes resultados em termos de sensibilidade e especificidade, além de exigir um tempo de deteção suficientemente pequeno para ser utilizado num sistema de aviso precoce (early-warning system). Começamos pela classificação de dados de forma Off-line, seguido da validação do classificador desenvolvido. Posteriormente, o processamento de dados é executado de forma contínua (On-line). Os algoritmos foram avaliados em conjuntos de dados reais, provenientes de estações sísmicas da Rede de Vigilância Sísmica de Portugal, e em aplicações reais da área de Sismologia (simulação de funcionamento em ambiente real). Apesar de apenas duas estações serem consideradas, verificou-se que utilizando a combinação de detetores, consegue-se uma percentagem de deteção idêntica para quando utilizado um único modelo (Abordagem OR) e o número de falsos alarmes para a combinação de modelos é quase inexistente (Abordagem AND). Os resultados obtidos abrem várias possibilidades de pesquisas futuras.

Estimating fuzzy membership functions parameters by the levenberg-marquardt algorithm

In previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems. In this paper the Levenberg-Marquardt technique is improved to optimise the membership functions in the fuzzy rules without Ruspini-partition. The class of membership functions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear functions as well.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

B-spline and neuro-fuzzy models design with function and derivative equalities

The design of neuro-fuzzy models is still a complex problem, as it involves not only the determination of the model parameters, but also its structure. Of special importance is the incorporation of a priori information in the design process. In this paper two known design algorithms for B-spline models will be updated to account for function and derivatives equality restrictions, which are important when the neural model is used for performing single or multi-objective optimization on-line.

Design of B-spline neural networks using a bacterial programming approach

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

Paralelização automática de algoritmos matriciais

Tese de dout., Engenharia Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2003

Antioxidant and phenolic compounds in sweet potato peels and leaves: food applications and health benefits

Tese de doutoramento, Ciências Biotecnológicas (Biotecnologia Alimentar), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Intelligent modelling of temperature propagation induced by therapeutic ultrasound

Dissertação de Mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015