서지주요정보
쌍 중요도를 가진 고차 신경망과 그의 광학적 구현 = High order neural networks with pair significance and their optical implementation
서명 / 저자 쌍 중요도를 가진 고차 신경망과 그의 광학적 구현 = High order neural networks with pair significance and their optical implementation / 손만진.
저자명 손만진 ; Sohn, Man-Jin
발행사항 [대전 : 한국과학기술원, 1993].
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8003439

소장위치/청구기호

학술문화관(문화관) 보존서고

DEE 93041

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초록정보

It is known that linear neural networks with bit significance concept have better performance than ones without bit significance in terms of memory capacity and error correction capability. The thesis demonstrate that pair significance concept can be introduced to quadratic neural networks, as an expansion of the bit significance concept in the linear neural networks. The proposed quadratic neural networks with pair significance may be trained by either Hebbian learning rule or error-dirven learning rule. The quadratic associative memory with pair significance, based on the Hebbian learning rule, is shown to have more memory capacity and better recall rate than the conventional one without pair significance. It is shown also by using the pair significance concept that some of the interconnections in quadratic associative memory are disturbing and redundant. By removing the redundant interconnections, more vectors can be stored and better recall rate can be achieved. This enables the number of interconnections in quadratic associative memories to be reduced, retaining the memory capacity and the recall rate. When the error-driven learning is applied to the quadratic neural network with pair significance, it becomes a quadratic training-by-adaptive-gain (TAG) model, a second order expansion of the linear TAG. This neural network allows important nonlinear mappings to be captured, an attribute that significantly improves the pattern classification power of the linear TAG model. Using liquid crystal light valve and the ground glass that can make random interconnections, the quadratic TAG was implemented.

서지기타정보

서지기타정보
청구기호 {DEE 93041
형태사항 iii, 82 p. : 삽도 ; 26 cm
언어 한국어
일반주기 부록 : αopt의 계산
저자명의 영문표기 : Man-Jin Sohn
지도교수의 한글표기 : 신상영
지도교수의 영문표기 : Sang-Yung Shin
학위논문 학위논문(박사) - 한국과학기술원 : 전기및전자공학과,
서지주기 참고문헌 : p. 74-78
주제 Neural networks (Computer science)
Signification (Logic)
Optical tooling.
Learning models (Stochastic processes)
신경 회로망. --과학기술용어시소러스
학습 모델. --과학기술용어시소러스
광학 설계. --과학기술용어시소러스
시행 착오 학습. --과학기술용어시소러스
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