Deep Learning Fuzzy Classifier

UDC 004.85(045) DOI:10.18372/1990-5548.60.13813
V. M. Sineglazov, R. S. Koniushenko. Deep Learning Fuzzy Classifier // Electronics and Control Systems, N 2(60) – Kyiv: NAU, 2019. – pp. 33–42.
It is considered a classification problem solution based on analysys of represented review. It’s shown that the neural networks has important advantages beside other methods, such as: classification using the nearest neighbor method, support vector classification, classification using decision trees, etc. Amoun of artifisial neural networks exists futher networks have the simplest structure, but the precission of the solution can be increased with help of deep learning approache, which is supposed the use of additional neural network for the solution of pretraining tasks(deep believe networks). It’s proposed new tophology wich consist of: Takagi-Sugeno-Kang fuzzy classifier and Limited Boltzmann Machine neural network. Despite on this thopology was proposed early in this article it’s carried out enough researches that permited to specify the learning algorithm. An example of proposed algorithm implantation is represented.
Index Terms—Neural network; fuzzy neural network; deep learning.
References: 20 names.