Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Macrofauna spatial differences within clam dredge-tracks and their implications for short-term fishing effect studies

In situ observations of clam dredging showed that the effects of the dredge on the benthic macrofauna may not be constant during a tow. A sand buffer forms in front of the gear approximately 10m after the beginning of a tow, and this pushes the sediment partially aside.In this study, we analyse differences in abundance, the number of taxa present, diversity, and evenness within sections of dredge-tracks in a disturbed, fished area and a non-fished area along the southern coast of Portugal. These areas were sampled by divers before and after dredge-fishing activity. At each site, three dredge-tracks were produced. These tracks were divided in three longitudinal sections 1start, middle and end) and two transverse sections 1track and edge). Six quadrats were used to sample macrofauna in each section of every track and edge. Our results show differences exist in macro- faunal distribution and abundance across sections of a dredge-track. These differences should be considered in any assessment of the short-term ecological impact of dredges on benthic macrofauna

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.

Cloning of the Bone Gla Protein gene from the teleost fish Sparus aurata (gilthead seabream). Molecular organization, developmental appearance and evolutionary implications

Tese de Doutoramento, Biologia Molecular, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2001