By Leon Bobrowski (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada (eds.)

This publication constitutes the refereed complaints of the eighth foreign convention on man made Intelligence and tender Computing, ICAISC 2006, held in Zakopane, Poland, in June 2006.

The 128 revised contributed papers provided have been rigorously reviewed and chosen from four hundred submissions. The papers are equipped in topical sections on neural networks and their purposes, fuzzy structures and their functions, evolutionary algorithms and their functions, tough units, type and clustering, photo research and robotics, bioinformatics and scientific functions, a number of difficulties of synthetic intelligence.

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Additional resources for Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings

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AN . The number of accumulators is equal to the number of neurons in a layer and it determines the level of parallelization of the calculations. The accumulator implementation requires the number of logic cells (LC) equal to its resolution. Because each LC contains only one register further reduction of the accumulator size is not possible. Thus a kind of minimal hardware representation for a single neuron has been achieved. Implementation of the Activation Function. As has been explained in the Chapter 2 the activation function is approximated by a series of linear segments.

All the input variables were used for the experiments. Table 2 depicts the detection performance of EDA-NN for test data set. 3 Intrusion Detection by PSO-NN For comparison purpose, a neural network classifier with structure {41-8-1} trained by PSO and with flexible bipolar sigmoid activation functions were also constructed using the same training data sets and then the neural network classifier was used on the test data set to detect the different types of attacks. All the input variables were used for the experiments.

10-100) leads to sufficiently accurate approximation. As an example a net consisting of 2 layers has been implemented for handwritten digit recognition. The original sigmoidal function has been approximated by three linear segments. Neural Network Implementation in Reprogrammable FPGA Devices 21 An attempt has been made to determine how the recognition effectiveness will be depend on the representation of the weight coefficients and what slope value of the central section of the sigmoidal function, responsible for the active part of the activation function, will ensure the recognition rate value similar to one achieved with real sigmoidal function.

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