Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task
2020
Yevgeniy Bodyanskiy, Anastasiia Deineko, Irina Pliss, Olha Chala

The probabilistic neuro-fuzzy system to solve the image classification-recognition task is proposed. The considered system is a “hybrid” of Specht’s probabilistic neural network and the neuro-fuzzy system of Takagi-Sugeno-Kang. It is designed to solve tasks in case of overlapping classes. Also, it is supposed that the initial data that are fed on the input of the system can be represented in numerical, rank, and nominal (binary) scales. The tuning of the network is implemented with the modified procedure of lazy learning based on the concept “neurons at data points”. Such a learning approach allows substantially reducing the consumption of time and does not require large amounts of training dataset. The proposed system is easy in computational implementation and characterised by a high classification speed, as well as allows processing information both in batch and online mode.


Keywords
Lazy learning; membership function; neural network; neuro-fuzzy system; probabilistic pattern recognition
DOI
10.7250/itms-2020-0002
Hyperlink
https://itms-journals.rtu.lv/article/view/itms-2020-0002

Bodyanskiy, Y., Deineko, A., Pliss, I., Chala, O. Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task. Information Technology and Management Science, 2020, Vol. 23, pp. 12-16. e-ISSN 2255-9094. Available from: doi:10.7250/itms-2020-0002

Publication language
English (en)
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