ProceedingsIEEE, 1997 - Artificial intelligence |
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Page 404
... variable is position error e ( t , ) = 0.25 , which belongs to the symmetric fuzzy set to the grade of membership μ ( e ( t1 ) ) = 0.3 . And , let the fuzzy set centre y . = 0.5 . Then , the input variable e ( t2 ) = 0.75 will have the ...
... variable is position error e ( t , ) = 0.25 , which belongs to the symmetric fuzzy set to the grade of membership μ ( e ( t1 ) ) = 0.3 . And , let the fuzzy set centre y . = 0.5 . Then , the input variable e ( t2 ) = 0.75 will have the ...
Page 405
... variable , j = 1 , ... , m , of the system . " Satisfaction of the first principle . The third and finale step of the creation of the new fuzzification interface process , is the satisfaction of the first principle . For this purpose we ...
... variable , j = 1 , ... , m , of the system . " Satisfaction of the first principle . The third and finale step of the creation of the new fuzzification interface process , is the satisfaction of the first principle . For this purpose we ...
Page 437
... variable " job scheduling [ 9 ] , by opposition to the " fixed " job scheduling where start and end times of tasks are known in advance . 4.1 State variable for a task For the formulation of Rm rj , dj , Sijk , Mj | — , we assume that ...
... variable " job scheduling [ 9 ] , by opposition to the " fixed " job scheduling where start and end times of tasks are known in advance . 4.1 State variable for a task For the formulation of Rm rj , dj , Sijk , Mj | — , we assume that ...
Contents
Innovations in Design and Integrated | 1 |
Creative and Innovative Manufacturing | 4 |
Y Fan and C Wu Tsinghua Univ | 7 |
Copyright | |
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according activities algorithm allows analysis application approach architecture assembly ATRT Automation called communication complete components considered constraints control system cost customers decision defined described determined dispatching distributed dynamic effective Engineering enterprise environment error estimated example exists Figure fixture flexible flow function given implementation important improvement industrial input integration interaction interface INTRODUCTION knowledge lead learning load locating machine manuals manufacturing means measurements method nets object obtained operation optimal output parameters performance Petri planning plant position possible presented problem proposed prototype real-time reference represents requirements robot rules scheduling selected sequence server shown shows simulation solution specific standard station structure task token tool transition unit University virtual