Artificial Intelligence Perspectives and Applications: by Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova,

By Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova, Zdenka Prokopova, Petr Silhavy

This quantity is predicated at the study papers awarded within the 4th laptop technological know-how online Conference.

The quantity synthetic Intelligence views and purposes offers new methods and strategies to real-world difficulties, and specifically, exploratory learn that describes novel ways within the box of synthetic intelligence. specific emphasis is laid on smooth traits in chosen fields of curiosity. New algorithms or equipment in various fields also are presented.

The laptop technology online convention (CSOC 2015) is meant to supply a world discussion board for discussions at the most up-to-date top of the range examine leads to all parts on the topic of computing device Science.

The addressed issues are the theoretical elements and functions of desktop technological know-how, man made Intelligences, Cybernetics, Automation regulate conception and software program Engineering.

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Extra info for Artificial Intelligence Perspectives and Applications: Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015), Vol 1: Artificial Intelligence Perspectives and Applications

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Step 19. = + 1. Go to Step 11. Step 20. If = 0, then go to Step 21, else go to Step 10. = . Step 21. For each : Using calculated values of ∶ with following sets are formed ∈ = , – total number of groups of processed rules. 2 27 Linguistic Variables Dependencies Fuzzy inference involves not only calculation of membership functions for each rule , but also defuzzyfication of rules’ goals. Sequence of goal variables defuzzyfication is not simply obtained from rules dependencies graph, because, each variable can be used in the right part of many rules with different value of ( ).

In order to solve this problem a method, which allows to define the order of the rules processing, is introduced. 1 Rules Dependencies The first stage is to generate the matrix of rules dependencies. Following algorithm is used: 26 O. Dolinina and A. Shvarts Step 1. Initialize zero-filled matrix with dimension of | | × | | (| | – cardinal number of set). Step 2. For each ∈ execute Step 3. Step 3. For each ∈ , ≠ execute Step 4. Step 4. If , ∈ , then , = 1 and , = −1. is an adjacency matrix of the directed graph of rules dependencies.

A number of reasoning methods and algorithms have been introduced and discussed since the appearance of fuzzy sets theory. Each of algorithms [3-10] differs from the point of view of sequence and type of operators applied in fuzzyfication, fuzzy inference and defuzzyfication stages. They also use fuzzy rules of different form. Ways of Increasing of the Effectiveness of the Making Decisions by Intelligent Systems 25 Vector I* Fuzzyfication Fuzzy inference Rule base R Defuzzyfication Vector G* Fig.

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