콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2012-08
- 저자명 Moonsoo Shin, Kwangyeol Ryu, and Mooyoung Jung*
- 학술지명 Expert Systems with Applications
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 3
논문 초록 (Abstract)
Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism.
