The Development of NNDFR Hybrid System Based on Fuzzy and Neural Approach for Matlab

Student: Lapidus Anna

Supervisor: Dmitry Bogolyubov

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Information Systems and Technology (Bachelor)

Final Grade: 9

Year of Graduation: 2016

The work focuses on NNDFR hybrid system for fuzzy inference implementation with the help of neural networks. The main goal of the work is to develop NNDFR system program for MATLAB. NNDFR system is analyzed, the use of hard k-means clustering with the cluster number adjustment and fuzzy c-means clustering for the system are considered. NNDFR functions based on hard and fuzzy clustering were implemented. Fuzzy clustering showed higher efficiency for the prognosis problems solution with NNDFR system. NNDFR designs much smaller rule base than ANFIS system while the error of NNDFR is comparable with the ANFIS error. The results of the work could be applied for various prognosis and control problems.

Full text (added May 28, 2016)

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