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 court cases of the seventh foreign Work-Conference on man made and typical Neural Networks, IWANN 2003, held in Ma?, Menorca, Spain in June 2003.

The 197 revised papers awarded have been conscientiously reviewed and chosen for inclusion within the e-book and handle the subsequent themes: mathematical and computational equipment in neural modelling, neurophysiological facts research and modelling, structural and useful versions of neurons, studying and different plasticity phenomena, complicated platforms dynamics, cognitive tactics and synthetic intelligence, methodologies for web layout, bio-inspired platforms and engineering, and purposes in a extensive number of fields.

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Extra 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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Fig. 2. Stochastic bit-stream generator and corresponding symbol A fundamental element of a digital stochastic processing system is a source of pseudorandom noise. A source of pseudorandom digital noise consists of a linear feedback shift register or LFSR, described by first in [lJ and by many others [2J, LFSRs are very practical as they can easily be constructed using standard digital components. Linear feedback shift registers can be implemented in two ways. The Fibonacci implementation consists of a simple shift register in which a binary-weighted modulo2 sum of the taps is fed back to the input.

The design was elaborated using VHDL [9]. The synthesis step generates an optimised netlist that is the mapping of the gate-level design into the Xilinx format: XNF. 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.

Fig. 5. ~::. __ 2,5 - Fig. 2. 2 Extended Multi-Layer Perceptron (XMLP) The XMLP is a feed-forward ANN with an input layer, up to two hidden layers and an output layer. The difference with respect to the MLP is that each layer can be configured as a two dimensional one and we can define the connectivity patterns of the neurons to restricted neighborhoods. This characteristic is very interesting for the speech recognition applications because in most cases we need to take into account a temporal environment of input vectors (for instance in phoneme recognition) or even it may be necessary to process the vectors corresponding to the whole pronunciation (complete word recognition).

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