Browsing by Author "Cabrita, Cristiano Lourenço"
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- Algoritmos construtivos para treino de redes neuronais b-splinePublication . Cabrita, Cristiano Lourenço; Ruano, A. E.Esta tese de dissertação do mestrado em Engenharia de Sistemas e Computação trata da descrição do funcionamento de dois algoritmos construtivos para definição automatica da estrutura e parâmetros de uma arquitectura de rede neuronal B-spline. o algoritmo ASMOD e Programação Genética para redes B-spline. Em primeiro lugar, descreve-se o Hincionamento e explora-se o desempenho obtido pelo algoritmo ASMOD, em função da utilização de diversos critérios de optimização não linear Apresentam-se igualmente, algumas alterações protagonizadas sobre o algoritmo original com o objectivo de aumentar a velocidade de execução. Seguidamente, expioram-se os princípios relacionados com as técnicas de Programação Genelica que embora tenham tido aplicação em diversas áreas cientificas, não tém sido frequentemente implementados em redes B-spline. Faz-se também uma abordagem multi-objectivo com o propósito de comparar o respectivo desempenho com o do algoritmo ASMOD. Adicionalmente, é apresentado um novo critério de optimização baseado no conhecimento prévio da complexidade de uma rede neuronal A separação dos parâmetros lineares dos não lineares permitem reformular o critério standard de optimização e obter um novo critério, que utilizado em conjunto com o algoritmo de Levenherg-Marqnardt exibe maior taxa de convergência. E também mostrado que o algoritmo Error-Back propagation, frequentemente usado com este propósito, obtém um desempenho pobre Todas as experiências realizadas utilizaram dados sobre exemplos académicos, a curva de titração de Ph numa concentração química e o problema de transformação inversa de coordenadas cartesianas para o ângulo de uma junta de um robô e dados recolhidos para auto-sintonia de um controlador P1D Todos os problemas são de natureza não linear.
- An hybrid training method for B-spline neural networksPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.
- B-spline and neuro-fuzzy models design with function and derivative equalitiesPublication . Cabrita, Cristiano Lourenço; Ruano, AntonioThe 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.
- B-spline and neuro-fuzzy models design with function and derivative equalitiesPublication . Ruano, Antonio; Cabrita, Cristiano LourençoThe 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.
- Bacterial memetic algorithm for fuzzy rule base optimizationPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Gedeon, Tamas D.; Ruano, Antonio; Kóczy, László T.; Fonseca, C. M.In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The Levenberg-Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques – the bacterial memetic algorithm – was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base.
- Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systemsPublication . Ruano, Antonio; Cabrita, Cristiano Lourenço; Oliveira, J. V.; Kóczy, László T.Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.
- Design of B-spline neural networks using a bacterial programming approachPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.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.
- Design of neuro-fuzzy models by evolutionary and gradient-based algorithmsPublication . Cabrita, Cristiano Lourenço; Ruano, A. E.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.
- Development and implementation of a smart charging system for electric vehicles based on the ISO 15118 standardPublication . Santos, Joni; Francisco, André; Cabrita, Cristiano Lourenço; Monteiro, Jânio; Pacheco, André; Cardoso, PedroThere is currently exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the associated charging demand. If, on the one hand, the introduction of energy generation from renewable energy sources can be used to meet that requirement, the intermittent nature of some of these sources will challenge the mandatory real-time equilibrium between generation and consumption. In order to use most of the energy generated via these sources, mechanisms are required to manage the charging of batteries in electric vehicles, according to the levels of generation. An effective smart charging process requires communication and/or control mechanisms between the supply equipment and the electric vehicle, enabling the adjustment of the energy transfer according to the generation levels. At this level, the ISO 15118 standard supports high-level communication mechanisms, far beyond the basic control solutions offered through the IEC 61851-1 specification. It is, thus, relevant to evaluate it in smart charging scenarios. In this context, this paper presents the development of a charge emulation system using the ISO 15118 communication protocol, and it discusses its application for demand response purposes. The system comprises several modules developed at both ends, supply equipment and electric vehicles, and allows the exchange of data during an emulated charging process. The system also includes human interfaces to facilitate interactions with users at both ends. Tests performed using the implemented system have shown that it supports a demand response when integrated with a photovoltaic renewable energy source. The dynamic adjustment to charging parameters, based on real-time energy availability, ensures efficient and sustainable charging processes, reducing the reliance on the grid and promoting the use of renewable energy.
- Estimating fuzzy membership functions parameters by the levenberg-marquardt algorithmPublication . Botzheim, J.; Cabrita, Cristiano Lourenço; Kóczy, László T.; Ruano, AntonioIn 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.