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.

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.

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.

Arquitectura indo-portuguesa na região de Cochim e Kerala: modelos e tipologias dos séculos XVI e XVII

Tese dout., História da Arte Modera, Universidade do Algarve, 2006

Modelos de avaliação de hipotecas comerciais: soluções analíticas

Tese de dout., Gestão, Faculdade de Economia, Universidade do Algarve, 2005

Purificação e caracterização de proteínas Gla nos modelos peixe e mamífero

Dissertação de Mestrado, Biologia Molecular e Microbiana, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2010

Estimação em pequenos domínios com modelos espaciotemporais de nível área

Tese dout., Métodos Quantitativos Aplicados à Economia e à Gestão, Universidade do Algarve, 2009

Análise de modelos de escoamento aplicados a águas superficiais e subterrâneas

Dissertação de mest., Recursos Hídricos, 2007, Faculdade de Engenharia de Recursos Naturais, Universidade do Algarve

Estimação em Pequenos Domínios com modelos que combinam Informação Seccional e Cronológica

O objectivo principal deste artigo consiste na proposta de um novo estimador para parâmetros de interesse em pequenos domínios com dados de nível área.

Modelos teóricos de análise do mercado de segunda habitação

O mercado de segundas habitações envolve investimentos elevados nos destinos, em novos empreendimentos turísticos, complexos de animação e complexos desportivos de apoio, sendo desde 2007 associado a um novo produto estratégico, o turismo residencial. Os turistas estrangeiros que estão associados a este segmento de procura turística (Turismo residencial), deslocam-se para os destinos, onde possuem a sua segunda habitação, por via aérea, sendo por isso muito importante estabelecer elos de ligação entre os vários stakeholders, nomeadamente entidades públicas e privadas que operam no destino, companhias aéreas e aeroportos, pois só assim se podem adequar estratégias individuais e em parceria entre todos os interessados, com o objectivo de captar clientes e até mesmo novos investimentos para a região. Os conceitos teóricos que se conhecem e dados recolhidos em 2007 e 2010, apontam para que os proprietários estrangeiros visitem várias vezes por ano o destino onde possuem uma segunda habitação, em períodos de menor procura turística, o que permite reduzir os índices de sazonalidade do destino. Neste artigo iremos abordar com mais detalhe a questão do mercado de segundas habitações e do produto estratégico que lhe está associado, o turismo residencial, e apresentar dois modelos teóricos que foram desenvolvidos para avaliar o processo de decisão de compra de um imóvel num destino (Procura), com as várias etapas que lhe são inerentes e características subjacentes, assim como a cadeia de valor do imobiliário residencial-turístico (Oferta), que nos permite identificar processos e intervenientes que nela participam, permitindo aferir a complexidade inerente a toda a envolvente e acima de tudo a dificuldade que existe no relacionamento entre actores/participantes.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Anytime information processing based on fuzzy and neural network models

In modern measurement and control systems, the available time and resources are often not only limited, but could change during the operation of the system. In these cases, the so-called anytime algorithms could be used advantageously. While diflerent soft computing methods are wide-spreadly used in system modeling, their usability in these cases are limited.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

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.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.