A 1-(S,T)-edge-connectivity augmentation algorithm by Enni S.

By Enni S.

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Fuzzy partitioning: Dividing the range of variability of each input variable xj into partly overlapping sets Xjk , k = 1, ... , defining the number of fuzzy sets and assigning a shape and values of parameters of the membership function of each set. 2. Defining the structure and parameters of functional consequents of individual rules. , n, – one TS rule for each sub-domain. 3. Selecting the method of calculation of activation levels of individual rules: choosing the product or the minimum operator.

In each of the functional and equipment layers of the control system, and even in a single direct control loop it is now possible to install more complex algorithms, including nonlinear algorithms and adaptive algorithms with automatic adaptation to assumed requirements. Apart from the control algorithms, it is also possible to perform, in the same local controllers, the tasks of technical diagnosis of measurement signals and of operation of the controllers themselves, with an automatic shift to redundant units when needed.

3. Combining the conclusions of all rules into one final conclusion. , y ∈ Y , where y is an output variable of a fuzzy system and Y is a fuzzy set created as a result of stages 1, 2 and 3 of the fuzzy reasoning. , for control, the obtained fuzzy value of the output variable should be further transformed into a crisp numerical form – then the following is performed: 4. Defuzzification – transforming a fuzzy value of the output variable into a numerical value. The fuzzy reasoning, and in particular its third stage, is much more simplified when consequents of all rules are not fuzzy, if they are crisp or functional.

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