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.
Highlights
With the rapid advance of computers, the digitization has swept in all fields of science and technology with special emphasize on modeling and identification
Let us consider four models of discrete time nonlinear dynamical systems [24] for single input single output (SISO) and multiple input and single output (MISO) system considered in this paper and they are described by difference equations (1)–(4) and Box-Jenkins time series data [10]
Four different nonlinear dynamical systems and one general benchmark problem known as Box-Jenkins model with time series data are considered
Summary
With the rapid advance of computers, the digitization has swept in all fields of science and technology with special emphasize on modeling and identification. Control of discrete time varying system using dynamical model is difficult To overcome this condition, new neural network approximation structure was developed to solve optimal tracking problem of nonlinear discrete time varying time system using reinforcement learning (RL) method [17]. An iterative learning control scheme was proposed for a class of nonlinear dynamic systems which includes holonomic systems as its subsets with linear feedback mechanism and feedforward learning strategies [23] In this proposed work we have used instar-outstar structure based CPN with Fuzzy Competitive Learning (FCL). (1) This paper contributes the approximation for a class of nonlinear dynamical systems using Fuzzy Competitive Learning Based Counter Propagation Network (FCPN).
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