Download Artificial Neural Nets Problem Solving Methods: 7th by Eva M. Ortigosa, Antonio Cañas, Eduardo Ros, Richard R. PDF

By Eva M. Ortigosa, Antonio Cañas, Eduardo Ros, Richard R. Carrillo (auth.), José Mira, José R. Álvarez (eds.)

The two-volume set LNCS 2686 and LNCS 2687 represent the refereed complaints of the seventh overseas Work-Conference on synthetic and common Neural Networks, IWANN 2003, held in Ma?, Menorca, Spain in June 2003.

The 197 revised papers offered have been rigorously reviewed and chosen for inclusion within the publication and deal with the next themes: mathematical and computational equipment in neural modelling, neurophysiological info research and modelling, structural and sensible versions of neurons, studying and different plasticity phenomena, complicated platforms dynamics, cognitive approaches and synthetic intelligence, methodologies for internet layout, bio-inspired structures and engineering, and functions in a large number of fields.

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Additional info for Artificial Neural Nets Problem Solving Methods: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part II

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The programming step consists of loading the generated bit-stream into the physical device. The design was programmed on device from VIRTEX-E family [10]; In Table I, we relate the size of a single neuron for different numbers of inputs, the net size and the net delay. In the reported hardware implementations, the neural networks are squared fully-connected feed-forward nets of neurons. So the number of 24 Nadia Nedjah and Luiza de Macedo Mourelle the net layers is the same as the number of neuron input.

4), based on each neuron's reactions to all possible inputs to it, up to the point in which the whole target ANN is converted into a set of interconnected digital circuit blocks (each block corresponding to one neuron) [2]. We used flip-flops because each neuron has to store its previous output, but the functionality of the neuron is enclosed in the Combinational Logic block. 6 The set of NSL non-monotonic operators is reported in [11]. ~ -go" I/O ak,e ~0 u ~ ! 13 I/O Fig. 4. The method proposes the substitution of the multipliers-adders-comparator's scheme by a combinational logic that implements a truth table Differently from pRAM (probabilistic RAM) and DPLM (Dynamically Progmmmable Logic Module) models and implementations [12] [13], the neuron mapped in hardware may have no learning capabilities.

The size of each layer and its partial connectivity are defined by six parameters in the following form: (1) x e y indicate the sizes of the axes, g and s define a group of nodes and a jump (step) between two consecutive groups in the X axis (gx, sx) and in the Y axis (gy, Sy). To a node with index i in the X axis of layer k, only are connected neurons of layer k-l of the i group of X axis, except if gx= x in layer k, in which case all the neurons of layer k are connected to all the neurons of X axis in layer k-l.

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