Download Computational Intelligence: Methods and Techniques by Leszek Rutkowski PDF
By Leszek Rutkowski
This ebook makes a speciality of a number of thoughts of computational intelligence, either unmarried ones and people which shape hybrid equipment. these strategies are this present day often utilized problems with synthetic intelligence, e.g. to approach speech and average language, construct specialist structures and robots. the 1st a part of the e-book offers tools of data illustration utilizing assorted recommendations, specifically the tough units, type-1 fuzzy units and type-2 fuzzy units. subsequent numerous neural community architectures are provided and their studying algorithms are derived. furthermore, the relatives of evolutionary algorithms is mentioned, specifically the classical genetic set of rules, evolutionary recommendations and genetic programming, together with connections among those options and neural networks and fuzzy structures. within the final a part of the publication, quite a few tools of knowledge partitioning and algorithms of automated information clustering are given and new neuro-fuzzy architectures are studied and in comparison. This well-organized sleek method of tools and strategies of clever calculations comprises examples and workouts in each one bankruptcy and a preface through Jacek Zurada, president of IEEE Computational Intelligence Society (2004-05).
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Methods of knowledge representation using rough sets specific subset. Let us denote the interesting set of object features of space U by the symbol Q. g. qi . What differentiates one object from another and makes other objects similar, these are the values of their features. Let us denote by Vq the set of values that the feature q can take. The value of feature q of the object x will be denoted as vxq . , vqxn . In this chapter the rough sets theory will be presented in the form of a series of definitions illustrated by examples.
1 will allow the reader an easier handling of them. 1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 2 Basic terms One of methods to present the information on objects characterized by the same set of features is the information system. 1 The information system is referred to an ordered 4-tuple SI = U, Q, V, f [1], Vq where U is the set of objects, Q is the set of features (attributes), V = q∈Q is the set of all possible values of features, while f : U ×Q → V is called the information function.
In other words, if at least one element of a given equivalence class belongs to the set X, then each element of this equivalence class belongs to the upper approximation of the set X. 5, elements of classes [x3 ]C , [x4 ]C , and [x8 ]C belong to the set XF , hence CXF = {x3 } ∪ {x4 , x5 } ∪ {x8 } = {x3 , x4 , x5 , x8 } . 57) The object x5 belongs to C-upper approximation of the set XF , even though it does not belong to XF , as the object x4 with identical values of features from set C, and therefore belonging to the same equivalence class, is an element of the set XF .