Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Associative dynamics in a chaotic neural network
Neural Networks
Self-organizing maps
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image which is noisy. The “inverse” of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. We propose a chaotic model of Pattern Recognition (PR) for the theory of “blurring”. The paper, which is an extension to a Companion paper [3] demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the Companion paper in which the chaotic PR system extracts the pattern from the input, this paper shows that the perception can be “blurred” if the dynamics of the chaotic system are modified. We thus propose a formal model, the Mb-AdNN, and present a rigorous analysis using the Routh-Hurwitz criterion and Lyapunov exponents. We also demonstrate, experimentally, the validity of our model by using a numeral dataset.