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On simulating one-trial learning using morphological neural networks
Institution:1. Department of Mathematics, University of the Punjab, Lahore, Pakistan;2. Department of Mathematics, Lahore College for Women University, Lahore, Pakistan;1. The Center for Chinese Modern City Studies, East China Normal University, China;2. School of Urban and Regional Science, East China Normal University, China;1. Department of Electrical Engineering, Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran;4. Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA
Abstract:“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.
Keywords:Machine learning  One-trial learning  Cognitive psychology  Simulation  Morphological neural networks  Morphological associative memories
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