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http://dx.doi.org/10.5391/JKIIS.2004.14.1.009

Learning for Environment and Behavior Pattern Using Recurrent Modular Neural Network Based on Estimated Emotion  

Kim, Seong-Joo (중앙대학교 일반대학원 전자전기공학부)
Choi, Woo-Kyung (중앙대학교 일반대학원 전자전기공학부)
Kim, Yong-Min (충청대학 컴퓨터학부)
Jeon, Hong-Tae (중앙대학교 일반대학원 전자전기공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.1, 2004 , pp. 9-14 More about this Journal
Abstract
Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.
Keywords
Emotion; Neural Network; Modular Network; Recurrent Neural Network;
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