@article{Zhengbing_Bodyanskiy_Tyshchenko_Samitova_2017, title={FUZZY DATA CLUSTERING IN THE RANK SCALE BASED ON A DOUBLE NEO-FUZZY NEURON}, url={http://ric.zntu.edu.ua/article/view/101031}, DOI={10.15588/1607-3274-2017-1-9}, abstractNote={Context. A task of data classification under conditions of clusters’ overlapping is considered in this article. Besides that, it’s assumed<br />that information to be processed is given in the rank scale.<br />Objective. It’s proposed to use a double neo-fuzzy neuron for classification which is a modification of a traditional neo-fuzzy neuron<br />with specially designed asymmetrical membership functions and improved approximating properties.<br />Method. The double neo-fuzzy neuron (just like the traditional one) is designated for processing data given the scale of natural numbers.<br />However, the situation may become complicated greatly if source data is not given in the numerical scale but in the ordinal one which is a<br />quite common case for a wide variety of practical tasks.<br />Results. A gradient minimization procedure with a variable learning step parameter was used for learning the double neo-fuzzy neuron.<br />The proposed approach to fuzzy classification for data given in the ordinal scale based on the double neo-fuzzy neuron which is learnt with<br />the help of a high-speed algorithm possesses additional smoothing properties. The clustering accuracy for a training sample and the test one as well as the system’s learning speed were measured during experiments. The proposed architecture of the double neo-fuzzy neuron is a sort of compromise between a traditional neo-fuzzy neuron and its extended modification. This architecture demonstrates good performance in those cases when the results’ accuracy has more influence compared to the elapsed time used for data processing.<br />Conclusions. Experimental implementation (for both artificial and real-world data) proved efficiency of the proposed techniques.<br />During the experiments, properties of the proposed system were studied which confirmed usability of the proposed system for a wide range of Data Mining tasks.}, number={1}, journal={Radio Electronics, Computer Science, Control}, author={Zhengbing, Hu and Bodyanskiy, Ye. V. and Tyshchenko, O. K. and Samitova, V. O.}, year={2017}, month={May} }