By P. J. Fleming, D. I. Jones

Laptop scientists have lengthy preferred that the connection among algorithms and structure is essential. often the extra really good the structure is to a selected set of rules then the extra effective may be the computation. The penalty is that the structure becomes lifeless for computing whatever except that set of rules. This message holds for the algorithms utilized in real-time automated keep watch over up to the other box. those complaints will supply researchers during this box with an invaluable updated reference resource of contemporary advancements.

**Read or Download Algorithms and Architectures for Real-Time Control 1991: Proceedings of the IFAC Workshop, Bangor, North Wa (IFAC Workshop Series) PDF**

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**Additional resources for Algorithms and Architectures for Real-Time Control 1991: Proceedings of the IFAC Workshop, Bangor, North Wa (IFAC Workshop Series)**

**Sample text**

50s -. In this and subsequen t figurestlie solid G ( s) = l + O . *l s line denote s the referenc e signa l and the dashe d line the outpu t signal . if 5 2 V 2 8 Time (sec. ) Fig. 6 . Contro l of a time-varyin g plan t The last exampl e consider s the contro l of a plan t with a varyin g transfe r function . As in the previou s example , the system was starte d in open loop , adaptiv e mode . The Uransfe r functio n of the plant is firstset at: Time (sec. ) Fig. 5 . Close d loop adaptatio n The syste m was initialize d in close d loop , with the PID parameter s manuall y chose n accordin g to the Ziegler Nichol s tunin g rule (Ziegler , 1942 ) for the specifie d plant .

Unfortunatel y it will take a lot of time to teach a neura l networ k system , but once it is taugh t it is fast and robus t leavin g the range of uncertaint y substantiall y greate r than that with adap tive control . Histor y has made clear it that neura l network s will hrst have to prove themselve s by solvin g problem s that have been previousl y impossibl e or very difficul t to solve, befor e being accepte d by industry . e. many propertie s presentl y exhibite d by humans . FAIL-SAF E CONTRO L 35 Neura l network s as well as Exper t system s and Adap tive contro l have their advantage s and disadvantages .

The most crucia l differenc e betwee n an exper t syste m and a conventiona l compute r progra m probabl y lies in the fact that the knowledg e in an exper t system is inter changeabl e and completel y separate d from the infer ence part. Rules and facts can be delete d or a d dde withou t changin g the whole program . Anothe r differ ence lies in the way solution s to a proble m are found . In a standar d compute r progra m they are found by means of an algorithmi c metho d while an exper t system a p p l ise rules which are likely to lead to satisfyin g answers .