Adaptive competitive self-organizing associative memory
This work presents the design of an adaptive competitive self-organizing associative memory (ACSAM) system for use in classification and recognition of pattern information. Volterra and Lotka's models of interacting species in biology motivated the ACSAM model; a model based on a system of nonl...
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Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2002-07, Vol.32 (4), p.461-471 |
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Sprache: | eng |
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Zusammenfassung: | This work presents the design of an adaptive competitive self-organizing associative memory (ACSAM) system for use in classification and recognition of pattern information. Volterra and Lotka's models of interacting species in biology motivated the ACSAM model; a model based on a system of nonlinear ordinary differential equations (ODEs). Self-organizing behavior is modeled for unsupervised neural networks employing the concept of interacting/competing species in biology. In this model, self-organizing properties can be implicitly coded within the systems trajectory structure using only ODEs. Among the features of this continuous-time system are: 1) the dynamic behavior is well-understood and characterized; 2) the desired fixed points are the only asymptotically stable states of the system; 3) the trajectories of ACSAM derived from the weight activities of the gradient system have no periodic or homoclinic orbits; and 4) the heteroclinic orbits that exist between equilibrium states are structurally unstable and can be removed by small perturbations. |
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ISSN: | 1083-4427 2168-2216 1558-2426 2168-2232 |
DOI: | 10.1109/TSMCA.2002.804789 |