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.

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.

Neuro-genetic PID autotuning: time invariant case

The Proportional, Integral and Derivative (PID) controllers are widely used in induxtrial applications. Their popularity comes from their robust performance and also from their functional simplicity.

Automatic tuning of PID controllers using a neuro-genetic system

Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.

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.

Ligand binding and signalling pathways of PTH receptors in sea bream (Sparus auratus) enterocytes

Whole animal studies have indicated that Ca2+ uptake by the gastrointestinal tract is regulated by the action of parathyroid hormone-related peptide (PTHrP) in teleost fish. We have characterised PTH receptors (PTHR) in piscine enterocytes and established, by using aminoterminal PTHrP peptides, the amino acid residues important for receptor activation and for stabilising the ligand/receptor complex. Ligand binding of 125I-(1–35tyr) PTHrP to the membrane fraction of isolated sea bream enterocytes revealed the existence of a single saturable high-affinity receptor (KD=2.59 nM; Bmax=71 fmol/mg protein). Reverse transcription/polymerase chain reaction with specific primers for sea bream PTH1R and PTH3R confirmed the mRNA expression of only the later receptor. Fugu (1–34) PTHrP increased cAMP levels in enterocytes but had no effect on total inositol phosphate accumulation. The aminoterminal peptides (2–34)PTHrP, (3–34)PTHrP and (7–34) PTHrP bound efficiently to the receptor but were severely defective in stimulating cAMP in enterocyte cells indicating that the first six residues of piscine (1–34)PTHrP, although not important for receptor binding, are essential for activation of the adenylate cyclase/phosphokinase A (AC-PKA)-receptor-coupled intracellular signalling pathway. Therefore, PTHrP in teleosts acts on the gastrointestinal tract through PTH3R and the AC-PKA intracellular signalling pathway and might regulate Ca2+ uptake at this site. Ligand-receptor binding and activity throughout the vertebrates appears to be allocated to the same amino acid residues of the amino-terminal domain of the PTHrP molecule.

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.

Investigating the role of Annexin A2 in Epidermal Growth Factor (EGF) induced signalling in cancer

Dissertação de mestrado, Qualidade em Análises, Faculdade de Ciências e Tecnologia, Universidade do Algarve; Universitat de Barcelona; Gdansk University of Technology, Universidad de Cádiz, Universitas Bergensis; 2015

Sintonia automática de controladores PID: uma abordagem neuro - genética

Dissertação de dout. em Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2004