scholarly journals Improved model reduction and tuning of fractional-order controllers for analytical rule extraction with genetic programming

2012 ◽  
Vol 51 (2) ◽  
pp. 237-261 ◽  
Author(s):  
Saptarshi Das ◽  
Indranil Pan ◽  
Shantanu Das ◽  
Amitava Gupta
2004 ◽  
Vol 16 (7) ◽  
pp. 1483-1523 ◽  
Author(s):  
Juan R. Rabuñal ◽  
Julián Dorado ◽  
Alejandro Pazos ◽  
Javier Pereira ◽  
Daniel Rivero

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.


2020 ◽  
Vol 30 (03) ◽  
pp. 2050044
Author(s):  
Fanqi Meng ◽  
Xiaoqin Zeng ◽  
Zuolei Wang ◽  
Xinjun Wang

In this paper, we investigate the dynamical characteristics of four-variable fractional-order Hindmarsh–Rose neuronal model under electromagnetic radiation. The numerical results show that the improved model exhibits more complex dynamical behavior with more bifurcation parameters. Meanwhile, based on the fractional-order Lyapunov stability theory, we propose two adaptive control methods with a single controller to realize chaotic synchronization between two coupled neurons. Finally, numerical simulations show the feasibility and effectiveness of the presented method.


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