Wyniki wyszukiwania - BazTech

1

Reducing the number of paths in a minimized project-network with given bounds on the durations of activities

Viattchenin D.

Operations Research and Decisions

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2015

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Vol. 25, No. 4

71--87

EN

This paper deals in a preliminary way with the problem of selecting the smallest possible number of dominant paths in a minimized project-network with given bounds on the permissible values of the durations of activities. For this purpose, a classification technique is proposed. This technique is based on a heuristic possibilistic clustering of interval-valued data. The basic concepts of heuristic possibilistic clustering are defined and methods for preprocessing interval-valued data are described. An illustrative example is considered in detail and some conclusions are formulated.

2

Extracting fuzzy classifications rules from three - way data

Kacprzyk J., Owsinski J. W., Viattchenin D. A.

Journal of Automation Mobile Robotics and Intelligent Systems

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2014

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Vol. 8, No. 2

47--57

EN

The paper deals in the conceptual way with the problem of extracting fuzzy classification rules from the three-way data meant in the sense of Sato and Sato [7]. A novel technique based on a heuristic method of possibilistic clustering is proposed. A description of basic concepts of a heuristic method of possibilistic clustering based on concept of an allotment is provided. A preprocessing technique for three-way data is shown. An extended method of constructing fuzzy classification rules based on clustering results is proposed. An illustrative example of the method’s application to the Sato and Sato’s data [7] is provided. An analysis of the experimental results obtained with some conclusions are given.

3

Automatic generation of fuzzy inference systems using heuristic possibilistic clustering

Viattchenin D. A.

Journal of Automation Mobile Robotics and Intelligent Systems

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2010

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Vol. 4, No. 3

36-44

EN

The interpretability and flexibility of fuzzy classification rules make them a popular basis for fuzzy controllers. Fuzzy control methods constitute a part of the areas of automation and robotics. The paper deals with the method of extracting fuzzy classification rules based on a heuristic method of possibilistic clustering. The description of basic concepts of the heuristic method of possibilistic clustering based on the allotment concept is provided. A general plan of the D-AFC(c)-algorithm is also given. A method of constructing and tuning of fuzzy rules based on clustering results is proposed. An illustrative example of the method's application to the Anderson's Iris data is carried out. An analysis of the experimental results is given and preliminary conclusions are formulated.

4

A direct algorithm of possibilistic clustering with partial supervision

Viattchenin D.

Journal of Automation Mobile Robotics and Intelligent Systems

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2007

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Vol. 1, No. 3

29-38

EN

Fuzzy clustering plays an important role in intelligent systems design and the respective methods constitute a part of the areas of automation and robotics. This paper describes a modification of a direct algorithm of possibilistic clustering that takes into account the information coming from the labeled objects. The clustering method based on the concept of allotment among fuzzy clusters is the basis of the new algorithm. The paper provides the description of basic ideas of the method and the plan of the basic version of a direct possibilistic-clustering algorithm. A plan of modification of the direct possibilistic-clustering algorithm in the presence of information from labeled objects is proposed. An illustrative example of the method's application to the Sneath and Sokal's two-dimensional data in comparison with the Gaussian-clustering method is carried out. Preliminary conclusions are formulated.

5

A new heuristic algorithm of fuzzy clustering

Viattchenin D. A.

Control and Cybernetics

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2004

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Vol. 33, no 2

323-340

EN

This paper deals with a new method of fuzzy clustering. The basic concepts of the method are introduced as resulting from the consideration of the fundamental fuzzy clustering problem. The paper provides the description of the general plan of the algorithm and an illustrative example. An analysis of the experimental results of the method's application to the Anderson's Iris data is carried out. Some preliminary conclusions and the ways of prospective investigations are given.