Parametric Shape-from-Shading by Radial Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Modern Control Systems
Multimodal decision-level fusion for person authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
RBFN restoration of nonlinearly degraded images
IEEE Transactions on Image Processing
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Object classification in 3-D images using alpha-trimmed mean radial basis function network
IEEE Transactions on Image Processing
Design of intelligent self-tuning GA ANFIS temperature controller for plastic extrusion system
Modelling and Simulation in Engineering
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For long time the optimization of controller parameters uses the well-known classical method such as the Ziegler-Nichols and the Cohen-Coon tuning techniques. Despite its effectiveness, these off-line tuning techniques can be time consuming especially for a case of complex nonlinear system. This paper attempts to show a great deal on how Metamodeling techniques can be utilized to tune the PID controller parameters quickly. Note that the plant use in this study is the cruise control system with 2 different models, which are the linear model and the nonlinear model. The difference between both models is that the disturbances were taken into consideration for the nonlinear model, but in the linear model the disturbances were assumed as zero. The Radial Basis Function Neural Network Metamodel is able to prove that it can minimize the time in tuning process as it is able to give a good approximation to the optimum controller parameters in both models of this system.