期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
MODELING AND PERFORMANCE ANALYSIS FOR THE SERIAL AND PARALLEL PRODUCTION SYSTEM BASED ON GSPN 被引量:2
1
作者 GaoJianhua HuXudong YangRuqing 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2004年第3期427-432,共6页
Differed from the existed applications of generalized stochastic Petri net(GSPN) theory in machine-tool manufacturing system, reliability computation of FMS, testabilityparameters determination and fault analysis, a n... Differed from the existed applications of generalized stochastic Petri net(GSPN) theory in machine-tool manufacturing system, reliability computation of FMS, testabilityparameters determination and fault analysis, a new idea of applying GSPN to model and performanceanalysis for the serial and parallel production system is proposed. And one typical discrete eventdynamic system (DEDS), turner-unit of palletizing system, is taken as a real case to research. Basedupon the established GSPN models, the working performances of serial and parallel layout arecompared. Furthermore, their differences of working mechanisms including feeding mechanism,coordinating mechanism and monitoring mechanism are discussed. Thus the theoretical basis which ishelpful to appraise layout plan and its reasonableness is provided. Meanwhile, the research resultsshow that parallel layout is more advantageous to greatly improve the operational speed ofproduction system than serial one. 展开更多
关键词 Serial and parallel production system Generalized stochastic Petriv net Discrete event dynamic system Palletizing system
下载PDF
Advanced Credit-Assignment CMAC Algorithm for Robust Self-Learning and Self-Maintenance Machine 被引量:1
2
作者 张蕾 LEEJay +1 位作者 曹其新 王磊 《Tsinghua Science and Technology》 SCIE EI CAS 2004年第5期519-526,共8页
Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be em- bedded in ma... Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be em- bedded in machine for real-time decision. This paper presents a credit-assignment cerebellar model articulation controller (CA-CMAC) algorithm to reduce learning interference in machine learning. The developed algorithms on credit matrix and the credit correlation matrix are presented. The error of the training sample distributed to the activated memory cell is proportional to the cell’s credibility, which is determined by its activated times. The convergence processes of CA-CMAC in cyclic learning are further analyzed with two convergence theorems. In addition, simulation results on the inverse kinematics of 2- degree-of-freedom planar robot arm are used to prove the convergence theorems and show that CA-CMAC converges faster than conventional machine learning. 展开更多
关键词 cerebellar model articulation controller machine learning self-maintenance machine self- learning
原文传递
ROBOT PROGRAMMING BY DEMONSTRATION FOR TASK IN CONTACT STATE
3
作者 WangQin QianJun WangChunxiang YangRuqing 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2004年第3期336-339,共4页
Robot programming by demonstration (PBD) system for task in which objectrequires contact with environment is built based on the controlling skill model. The skill isdescribed in three aspects: contact state classifier... Robot programming by demonstration (PBD) system for task in which objectrequires contact with environment is built based on the controlling skill model. The skill isdescribed in three aspects: contact state classifier, acquirement of contact states sequence andcontrolling transition between states. The classifier is developed with the support vector machineby using force sense. Sequence of states is obtained from the force signal of demonstration by theevent trigger. The velocity command of transition is achieved by linearization method. The PBDsystem is successfully built with robot controller with open architecture. 展开更多
关键词 ROBOT Programming by demonstration Contact state Support vector machine
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部