• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.215-218
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• 2001

최근 지능 정보 처리에 대한 연구가 여러 분야에서 활발히 진행되고 있으며, 불확실하고 복잡한 동적 환경에 적응할 수 있는 계산 모델로 점차 영역을 확장해 가고 있다. 따라서, 본 논문에서는 다치 논리함수의 차분의 성질을 이용한 다치 오토마타 모델을 제시한다. 즉, 입력 스트링 상태를 다치 함수의 치에 사상시키고, 다치 함수의 차분의 성질을 이용하여 상태의 전이에 적용시켜 동적 변화에 자율적으로 적응할 수 있도록 하였다. 그러므로 다치 오토마타는 동적으로 변화하는 시스템을 모델링 하는데 광범위하게 응용될 수 있을 것이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.13-18
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• 2009

Usually, human emotions are vague and change diversely on the basis of the stimulus from the outside. Plutchik classified the fundamental behavioral patterns into eight patterns, named each of them a genuine emotion, and furthermore suggested mixed emotions using a combination of genuine emotions. In this paper, we propose a method for processing Plutchik's emotion model using Multiple Valued Logic(MVL) Automata Model which utilizes the properties of difference in Multiple Valued Logic functions. This proposed emotion processing model can be widely applied to the analysis and processing of emotion data.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.10-16
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• 1996

In this paper, we construct the state table of the automata according to input, state, and change of state and transform that state table into symbolic multi-valued logic formula. Also, we propose an adaptive automata which adapts dynamically change of state aqording to the input string of automata by using the properties of derivative about the symbolic multi-valued logic function. And we analyze the properties of the adaptive automata.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.701-708
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• 2001

Recently, the research on intelligence of computer has actively been under way in various areas and gradually extended to adapt to uncertain and complex environments. In this paper, we propose the MVL-Neural Valued Logic. Also, we verify that the MVL-Automata can be implemented to Neural Network and the MVL-Neural Network Model can be a simulator by MVL-Automata. Therefore, we propose that the MVL-Neural Network Model can be widely used in such area, as intelligent system or modeling of brain. In particular, the MVL-Neural Network is expected to be used as core technology of next generation computer.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.311-314
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• 2001

Recently, research on Intelligent Information Process has actively been under way JD various areas and gradually extended to be adaptive to uncertain and complex dynamic environments. This paper presents a Multiple Valued Logic Automata(MVL-Automata) Model, utilizing properties of difference in a Multiple Valued Logic function. That is, MVL-Automata is able to be autonomously adapted to dynamic changing since an input stling is mapped to the value of a Multiple Valued Logic function and the property of difference in a Multiple Valued Logic function is applied to state transition. Therefore, Multiple Valued Logic Automata can be widely applied to the modeling dynamically of changing environments.