Titlebook: Artificial Intelligence and Soft Computing — ICAISC 2004; 7th International Co Leszek Rutkowski,Jörg H. Siekmann,Lotfi A. Zadeh Conference

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Visualization of Hidden Node Activity in Neural Networks: I. Visualization Methodscusses how to visualize such multidimensional data, introducing a new projection on a lattice of hypercube nodes. It also discusses what type of information one may expect from visualization of the activity of hidden and output layers. Detailed analysis of the activity of RBF hidden nodes using this

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Rough Set Approach to Incomplete Datalost values (a value was recorded but it is unavailable) and “do not care” conditions (the original values were irrelevant). Through the entire paper the same calculus, based on computations of blocks of attribute-value pairs, is used. Complete data are characterized by the indiscernibility relation

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Hormones

Support of Natural, by Artificial, Intelligence Using Utility as Behavioral Goal first factor represents the long-term expected profit of generalized capital investment..The second factor represents the worse case profit necessary for survival of crises. Using that concept it is possible to support the individual decisions connected with choosing the best alternative. It enable

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Top-Down Selective Attention for Robust Perception of Noisy and Confusing Patternsous knowledge and filters out irrelevant sensory signals for high-confidence perception of noisy and confusing signals. The TDSA is modeled as an adaptation process to minimize the attention error, which is implemented by the error backpropagation algorithm for the multilayer Perceptron classifiers.

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Inference Rules and Decision Rulesexpressed by true implication, ”. Φ . Ψ”. Then basing on true . Φ we arrive at true . Ψ (MP), or from negation of true conclusion Ψ we get negation of true premise Φ (MT)..In reasoning from data (data mining) we also use rules ”. Φ . Ψ”, called ., to express our knowledge about reality, but in this

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