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

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期刊全称Bio-Inspired Systems: Computational and Ambient Intelligence
期刊简称10th International W
影响因子2023Joan Cabestany,Francisco Sandoval,Juan M. Corchado
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe
影响因子This volume presents the set of final accepted papers for the tenth edition of the IWANN conference “International Work-Conference on Artificial 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 (LNCS 540, 1991), the conference has evolved and matured. The list of topics in the successive Call for - pers has also evolved, resulting in the following list for the present edition: 1. Mathematical and theoretical methods in computational intelligence. C- plex and social systems. Evolutionary and genetic algorithms. Fuzzy logic. Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines. 2. Neurocomputational formulations. Single-neuron modelling. Perceptual m- elling. System-level neural modelling. Spiking neurons. Models of biological learning. 3. Learning and adaptation. Adaptive systems. Imitation learning. Reconfig- able systems. Supervised, non-supervised, reinforcement and statistical al- r
Pindex Conference proceedings 2009
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Thomas Schwinger,Hans Jörg Burmeister rate is 163.51 MOPS for 11x11 kernels, in a Xilinx Spartan 3 400 FPGA with a 50MHz clock. Formulations, hardware architecture, operation examples and performance comparison with frame-based convolution processors are presented and discussed.
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Bio-Inspired Systems: Computational and Ambient Intelligence10th International W
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Eric Linhart,Bernhard Kittel,André Bächtigeron the hypercube [0,1]., and the class of functions computed by artificial neural networks using a single hidden layer, each unit evaluating a sigmoidal activation function. Our lower bounds are stated in terms of an invariant that measures the oscillations of functions of the space . around the ori
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https://doi.org/10.1007/978-3-658-05008-5emporal patterns generated by neuronal activity in a set of coupled neurons. The method here presented allows a quantitative characterization of spatio-temporal patterns and is based on the behavior of a compression-like scheme. The wavelet-based method is faster than the two-dimensional spectral me
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https://doi.org/10.1007/978-3-531-19606-0ers-Take-All) network. The mathematics of circuit design and operation is discussed . two basic tools: the Liapunov function ensuring the gradient like behavior and the rational choice of the weights that stands for network training to ensure order-preserving trajectories. Dynamics and behavior at e
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