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Implementation of neural network for Generalized predictive control: A comparison between a newton Raphson and Levenberg Marquardt implementation



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An Efficient implementation of Generalized predictive control using Multi-layer feed forward neural network as the plant's nonlinear model is presented. Two algorithm i.e. Newton Raphson and Levenberg Marquardt algorithm are implemented and their results are compared. The details about this implementation are given. The utility of each algorithm is outlined in the conclusion. In using Levenberg Marquardt algorithm, the number of iteration needed for convergence is significantly reduced from other techniques. This paper presents a detail derivation of the Neural Generalized predictive control algorithm with Newton Raphson and Levenberg Marquardt as the minimization algorithm. A Simulation result of Newton Raphson and Levenberg Marquardt algorithm are compared. Levenberg Marquardt algorithm shows a convergence of a good solution. The Performance comparison of these two Algorithms also given in terms of ISE and IAE. © 2008 IEEE.

Neural generalized predictive control (1 items found) | Generalized predictive control (2 items found) | Efficient implementation (8 items found) | Multi-layer feed forward (4 items found) | Performance comparison (65 items found) | Simulation result (356 items found) | Algorithms (2048 items found) | Convergence of numerical methods | Levenberg-Marquardt algorithm | Predictive control systems | Model predictive control | Minimization algorithms | Number of iterations | Levenberg-Marquardt | Computer science | Non-linear model | Neural networks | Newton-Raphson | Simulators |

ต้นฉบับข้อมูล : scopus