Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe

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https://doi.org/10.1007/978-3-658-05008-5thods for finding different rhythms on spatio-temporal patterns, as it has a computational complexity .(. ×.) for each 2D-frame of the spatio-temporal pattern. The method also provides a easy method for classifying different qualitative behaviors of the patterns.

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Eric Linhart,Friedrich Hedtrich complex time series and scale-free distributions for the escape times of the system. This finding may be of interest to understand nonlinear phenomena observed in real neural systems and to design bio-inspired artificial neural networks with convenient complex characteristics.

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Rebecca B. Morton,Kenneth C. Williamsatrix with adequate values for missing entries and, second, to improve the consistency matrix to an acceptable level. In this paper a model based on Multi-layer Perceptron (MLP) neural networks is presented. This model is capable of completing missing values in AHP pairwise matrices and improving its consistency at the same time.

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‘Divide the Dollar’ Using Voting by Vetorate the ability of the maximal variation method to recover relevant variables from the given ones. A real life study concentrates on a breast cancer dataset containing clinical variables. The results indicate a better performance for the proposed method compared to Cox regression with an .. regularization scheme.

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0302-9743 cial neural Networks” held in Salamanca (Spain) during June 10–12, 2009. IWANN is a biennial conference focusing on the foundations, theory, models and applications of systems inspired by nature (mainly, neural networks, evolutionary and soft-computing systems). Since the first edition in Granada (L

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Eric Linhart,Bernhard Kittel,André B?chtigeral activation function. Our lower bounds are stated in terms of an invariant that measures the oscillations of functions of the space . around the origin. As an application we estimate the minimal number of neurons required to approximate bounded functions satisfying uniform Lipschitz conditions of order . with accuracy ..