COMPUTING SYSTEMS AND NETWORKS
DATA PROCESSING AND ANALYSIS
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
S. A. Karatach, V. G. Sinuk Machine Learning of a Fuzzy System with Linguistic Inputs Using Parallel Technologies
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
S. A. Karatach, V. G. Sinuk Machine Learning of a Fuzzy System with Linguistic Inputs Using Parallel Technologies
Abstract. 

Fuzzy systems with fuzzy inputs can be used in tasks where it is necessary to make predictions for data objects that have qualitative characteristics. However, building an optimal block of rules for such a system may be non-trivial, including due to the requirement to have a certain depth of knowledge in the subject area. In this situation, there is a need to automate the process of compiling the rule base, that is, to build a machine learning algorithm. In this paper, we propose to use a genetic (evolutionary) algorithm as such an algorithm. It describes both the specifics of using this family of algorithms for training a fuzzy system, and the features of parallel implementation of the learning process using CUDA technology.

Keywords: 

a logical type of inference, a decomposition theorem, evolutionary learning, parallel computations.

PP. 60-69.

DOI 10.14357/20718632210306
 
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