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

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https://doi.org/10.1007/978-3-031-21444-8ous 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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Informationsmanagement in Theorie und Praxisthe solution of sequences of different pattern recognition tasks. Each time after the task change, we start training from previous perceptron weight vector. We found that large values of components of the weight vector decrease the gradient and learning speed. A noise injected into the desired outpu
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https://doi.org/10.1007/978-94-017-5616-7on can be found as next step on the way starting from capturing of the images in digital form as two–dimensional data structures, next going throw images processing as a tool for enhancement of the images visibility and readability, applying images analysis algorithms for extracting selected feature
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The Right to Autonomy in Religious Affairsties can be obtained by adding a finite impulse response filter to the neuron. A combinatorial algorithm is used for selecting the network structure. If a linear activation function is used, the universal approximation capabilities of these networks are easy to prove. To show the applicability of su
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https://doi.org/10.1007/b98109Artificial intelligence; algorithms; classification; evolution; evolutionary algorithm; fuzzy; fuzzy syste
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978-3-540-22123-4Springer-Verlag Berlin Heidelberg 2004
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