Titlebook: Artificial Intelligence and Soft Computing; 12th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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Designing of State-Space Neural Model and Its Application to Robust Fault Detectionf the Unscented Kalman Filter to the training of the designed neural model is also shown. The final part of this work provides an illustrative example of the application of the proposed methodology to the identification and robust fault detection of the tunnel furnace.

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A Study on the Scalability of Artificial Neural Networks Training Algorithms Using Multiple-Criteriarises not simply accuracy but several other measures regarding computational resources. In order to compare the scalability of algorithms it is necessary to establish a method allowing integrating all these measures into a single rank. These methods should be able to i) merge results of algorithms t

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Testing the Generalization of Feedforward Neural Networks with Median Neuron Input Functionction (MIF). In these networks, proposed in our previous work, the signals fed to a neuron are not summed but a median of input signals is calculated. The MIF networks were designed to be fault tolerant but we expect them to have also improved generalization ability. Results of first experimental si

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Biological Plausibility in an Artificial Neural Network Applied to Real Predictive Tasksdels are based on existing knowledge of neurophysiological processing principles. Research in this field has increased in the last few years and has generated new viewpoints, propositions and models that are closer to the known features of the human brain. Some researchers have recently focused thei

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Random Sieve Based on Projections for RBF Neural Net Structure Selectione, we consider the case of a deficit in the admissible number of observations (learning sequence) in comparison with a much larger number of candidate terms. The proposed approach is based on a random sieve that aims at selecting only necessary RBF’s by a hierarchy of a large number of random mixing

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Eigenvalue Spectra of Functional Networks in fMRI Data and Artificial Modelstra (set of eigenvalues of the graph adjacency matrix) of both networks turn out to obey similar decay rate and characteristic power-law scaling in their middle parts. This extends the set of statistics, which are already confirmed to be similar for both neural models and medical data, by the graph