Titlebook: Artificial Intelligence and Soft Computing – ICAISC 2006; 8th International Co Leszek Rutkowski,Ryszard Tadeusiewicz,Jacek M. ?ur Conferenc

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Estimation of Distribution Algorithm for Optimization of Neural Networks for Intrusion Detection Sysg intrusions in a network. Results are then compared with Particle Swarm Optimization (PSO) based neural classifier and Decision Trees (DT). Empirical results clearly show that evolutionary computing techniques could play an important role in designing real time intrusion detection systems.

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狂亂

Sum and Product Kernel Regularization Networksive new approximation schemas – Sum Kernel Regularization Network and Product Kernel Regularization Network. We present some concrete applications of the derived schemas, demonstrate their performance on experiments and compare them to classical solutions. For many tasks our schemas outperform the classical solutions.

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On Interpretation of Non-atomic Values and Induction of Decision Rules in Fuzzy Relational Databases addition, the database extends classical equivalence relations with fuzzy proximity relations, which provide users with interesting analytical capabilities. In this paper we concentrate on both of these properties when proposing new approaches to interpretation of non-atomic values for decision making purposes.

蝕刻術(shù)

https://doi.org/10.1007/978-94-007-6137-7es used and the network operation speed. Therefore the presented solution can be applied both for implementation of big networks in small FPGA devices and for implementation working in real time, for which high operation speed is required.

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Martin Lehmann,Per Christensen,Bj?rn Johnsonce, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system.

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Chris Sauer,Gray Southon,C. N. G. Dampneyand numerical stability. In addition, it is less sensitive to start-up parameters, such as initial weights and covariance matrix. It has also good model prediction ability and need less training time. The effectiveness of the proposed algorithm is demonstrated via two nonlinear systems modeling and identification examples.

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Neural Network Implementation in Reprogrammable FPGA Devices – An Example for MLPes used and the network operation speed. Therefore the presented solution can be applied both for implementation of big networks in small FPGA devices and for implementation working in real time, for which high operation speed is required.