By Professor Zdzislaw Bubnicki PhD (auth.)
A unified and systematic description of research and determination difficulties inside of a large category of doubtful platforms, defined through conventional mathematical equipment and by means of relational wisdom representations.
With certain emphasis on doubtful keep an eye on platforms, Professor Bubnicki offers a special method of formal types and layout (including stabilization) of doubtful structures, in response to doubtful variables and similar descriptions.
• advent and improvement of unique suggestions of doubtful variables and a studying procedure which includes wisdom validation and updating.
• Examples in regards to the regulate of producing platforms, meeting procedures and activity distributions in desktops point out the probabilities of sensible functions and ways to choice making in doubtful systems.
• comprises distinctive difficulties similar to acceptance and keep watch over of operations lower than uncertainty.
If you have an interest in difficulties of doubtful keep watch over and selection help structures, this may be a worthwhile addition in your bookshelf. Written for researchers and scholars within the box of keep an eye on and data technology, this e-book also will gain designers of knowledge and regulate systems.
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Extra info for Analysis and Decision Making in Uncertain Systems
D We can consider other approaches to the determinization of KP or KD under consideration, consisting in two successive determinizations: the determinization at the relational level analogous to that described in Sect. 4 and the determinization concerning uncertainty described by the probability distribution of :X at the second level. A. Determinization for the given D y In this case we may use the mean value uM in the determinization of the relational and random decision algorithm < Du (z; x )Jx > ~ KD 1 at the first level: uM = fudu·[ Du(z;x) fdur1~ct>d(z,x).
In Sect. 1, a very short description of random variables is given to introduce the notation and to bring together formalisms concerning random, uncertain and fuzzy variables in a unified framework. 1 Random Variables and Probabilistic Forms of Knowledge Representations A random variable :X is defined by a set of variables X ~Rk (multidimensional vector space) and a probability distribution. X2 , ... e. the probabilities that :X =xj for j= 1,2, ... ,m. e. and :x
L - 1,2, ... , ' j = l,2, ... 4) The forms of KP presented above may be called non-parametric descriptions of the uncertain plant. Rk denotesan unknown vector parameter which is assumed to be a value of a random variable :X described by the probability density fx(x) (or by the respective form of the probability distribution in a discrete case). In this case KP = <
for a functional plant and KP =