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

Abstract

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.

Використання Штучного Інтелекту Безпілотних Суден Для Визначення Перешкод При Плаванні

“…Приклад застосування методу скелетування наведено на рисунку 1 для вільного простору, яка є геометричним місцем усіх точок, рівновіддалених від двох або декількох перешкод [7].…”

Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method

“…In [17], a transformation of vessel kinematics to the Serret-Frenet frame is introduced by exploring an extra degree of freedom by controlling explicitly the progression rate of the virtual target along the path and overcomes the major singular problem; approach angle is introduced for controller design via backstepping method. Neural networks are introduced to enhance system stability and transient performance, which can handle the known dynamics and uncertainties of systems well [18–20]. Particularly in [12] a single hidden layer neural network (SHLNN) is adopted to obtain the adaptive signal online, but the choice of the single hidden layer neural network is limited by the number of hidden layer node selections that will affect the online learning speed and accuracy and cannot produce a better estimation effect on the fast changing disturbances.…”

T-ARX Model for Nonlinear Discrete System Prediction Based on A Hybrid Modelling Method

“…18 to show the prediction error. (Bartholomew, 1971) 0.2020 Sugeno and Tanaka(1991) 0.0680 Hanli et al, (2005) 0.0660 ARX (Xu, et al, 2020) 0.0616 BP Network 0.0571 Gaweda and Zurada (2003) 0.0550 Euntai et al, (1997) 0.0550 Rezaee and Zarandi (2010) 0.0512 Xu et al, (2020) 0.0462 Sakhre et al, (2015) 0.0443 T-ARX 0.0372…”