On simulating one-trial learning using morphological neural networks. (January 2019)

• Record Type: Journal Article
• Authors: Feng, Naiqin; Sun, Bin
• Is Part Of: Cognitive systems research. Volume 53(2019)
• Page Start: 61
• Page End: 70
• Publication Date: 2019-01
• Subjects: Machine learning -- One-trial learning -- Cognitive psychology -- Simulation -- Morphological neural networks -- Morphological associative memories; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.05.003
• Languages: English
• ISSNs: 1389-0417

Abstract

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.