Conference paper
Hierarchical Fuzzy identification using gradient descent and recursive least square method
Dept. Of Control Eng., Islamic Azad Univ., Tehran, Iran1
Dept. of Electr. & Control Eng., Semnan Univ., Semnan, Iran2
Dept. of Control Eng., K.N. Toosi Univ. of Tech., Tehran, Iran3
In this paper, the parameters of hierarchical fuzzy systems are trained using the simultaneous use of Gradient Descent (GD) for nonlinear parameters and recursive least square (RLS) algorithm for linear parameters. One of the most effective ways to overcome the curse of dimensionality of fuzzy systems is the use of hierarchical fuzzy systems (HFS).
Considering the learning abilities of fuzzy systems, two learning algorithms GD and GD+RLS have been used to teach HFS. The results of simulation show that, the use of HFS causes the decrease in the number of rules and results in better performance in identification. In addition, when GD+RLS algorithm is used for learning HFS, it produces better results when it is compared to GD algorithm.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2013 |
| Pages: | 1-5 |
| Proceedings: | 2013 3rd IEEE International Conference on Computer, Control & Communication (IC4) |
| ISBN: | 1467358843 , 1467358851 , 1467360112 , 9781467358842 , 9781467358859 and 9781467360111 |
| Types: | Conference paper |
| DOI: | 10.1109/IC4.2013.6653750 |
Chemical reactors Computational modeling Fuzzy logic Fuzzy systems GD method GD+RL learning algorithms Gradient Descent HFS Hierarchical Fuzzy Systems Least squares methods Mathematical model RLS algorithm Recursive Least Square Training fuzzy set theory fuzzy systems gradient descent method gradient methods hierarchical fuzzy identification system learning (artificial intelligence) least squares approximations nonlinear parameters recursive least square method
