Comparative Analogy of Neural Network Modeling Versus Ant Colony System (Algorithmic and Mathematical Approach)
• 2013
Publication Information
Authors
Hassan M. H. Mustafa
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publication.type
International
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Abstract
This piece of research addresses an interdisciplinary,
challenging and interesting learning issue. More specifically, it
deals with analytical and quantitative study comparing two
suggested naturally inspired behavioral learning systems. In
other words, this study presents an investigational comparison
between two diverse realistic models of biological systems.
Namely, these systems are associated with learning at
mammalian (Pavlovian) and Ant Colony Systems. Introduced
investigations have includedbehavioral responsive functions, for
learning process contributed inside brain neural system (number
of neurons), as well as Ant Colony Optimization ACO.
Additionally, this work revealed an interesting analogy between
both suggested systems considering adaptive mathematical
learning equations and algorithms. Moreover, analogous results
have been introduced for suggested system versus animal
learning performance considering spikes (pulsed) neurons
approach.
challenging and interesting learning issue. More specifically, it
deals with analytical and quantitative study comparing two
suggested naturally inspired behavioral learning systems. In
other words, this study presents an investigational comparison
between two diverse realistic models of biological systems.
Namely, these systems are associated with learning at
mammalian (Pavlovian) and Ant Colony Systems. Introduced
investigations have includedbehavioral responsive functions, for
learning process contributed inside brain neural system (number
of neurons), as well as Ant Colony Optimization ACO.
Additionally, this work revealed an interesting analogy between
both suggested systems considering adaptive mathematical
learning equations and algorithms. Moreover, analogous results
have been introduced for suggested system versus animal
learning performance considering spikes (pulsed) neurons
approach.
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