Optimal Control of Nonlinear Dynamical Systems Using Direct Solution of Neural Network Controllers

2002 ◽  
Author(s):  
Seid Pourtakdoust ◽  
M. Jalali ◽  
R. Ghorbani ◽  
A. Zahedi
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Direct Solution ◽  
Nonlinear Dynamical ◽  
Neural Network Controllers

scholarly journals Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Vandana Sakhre ◽  
Sanjeev Jain ◽  
Vilas S. Sapkal ◽  
Dev P. Agarwal
Keyword(s):  
Neural Network ◽  
Dynamical Systems ◽  
Time Series Data ◽  
Nonlinear Dynamical Systems ◽  
Back Propagation ◽  
Network Control ◽  
Series Data ◽  
Nonlinear Dynamical ◽  
Single Output ◽  
Propagation Network

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

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