지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2016.10
- 수록면
- 778 - 781 (4page)
이용수
초록· 키워드
오류제보하기
Mill is acknowledged as main crushing equipment in the ore dressing and smelting process. In addition, the mill load is considered as one of the critical parameters during the operation of crushing equipments. In light of complexity, nonlinearity and uncertainty of the mill load control, an improved neural network control strategy based on chaotic particle swarm optimization was proposed. To realize the quick and accurate control of mill load, the neural network weight was optimized using the chaotic particle swarm, furthermore, the slower BP neural network convergence rate and larger probability to fall into minimum value were improved. Ultimately, the simulation results indicated that the control performance such as stability of mill load control was obviously improved using the proposed strategy.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. MILL LOAD CHARACTERISTICS
Ⅲ. CHAOTIC PSO NEURAL NETWORK PID CONTROL SYSTEM
Ⅳ. CHAOTIC PARTICLE SWARM NEURAL NETWORK
Ⅴ. SIMULATION RESEARCH
Ⅵ. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2017-003-001866697