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Cystic fibrosis (CF) is a fatal genetic disease caused by mutations in cftr, a gene encoding a PKA-regulated Cl(-) channel. The most common mutation results in a deletion of phenylalanine at position 508 (DeltaF508-CFTR) that impairs protein folding, trafficking, and channel gating in epithelial cells. In the airway, these defects alter salt and fluid(More)
This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process.(More)
To date, clustering techniques have always been oriented to solve classification and pattern recognition problems. However, some authors have applied them unchanged to construct initial models for function approximators. Nevertheless, classification and function approximation problems present quite different objectives. Therefore it is necessary to design(More)
We show that neurons that underlie rhythmic patterns of electrical output may be identified by optical imaging and frequency-domain analysis. Our contrast agent is a two-component dye system in which changes in membrane potential modulate the relative emission between a pair of fluorophores. We demonstrate our methods with the circuit responsible for(More)
In previous work we have derived a magnitude termed the 'Mean Squared Sensit-ivity' (MSS) to predict the performance degradation of a MLP affected by perturbations in different parameters. The present Letter continues the same line of researching, applying a similar methodology to deal with RBF networks and to study the implications when they are affected(More)
A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train(More)
In this paper, a novel approach is presented to fine tune a direct fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pretraining, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and(More)
This paper presents a new approach to the pagination problem. This problem has traditionally been solved ooine for a variety of applications like the pagination of Yellow Pages or newspapers, but since these services have appeared in Internet, a new approach is needed to solve the problem in real time. This paper is concerned with the problem of paginating(More)
We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with(More)