A bioreactor benchmark for adaptive network-based process control
Neural networks for control
A challenging set of control problems
Neural networks for control
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Nonlinear identification of pneumatic servo-drive
International Journal of Modelling and Simulation
An adaptive recurrent fuzzy system for nonlinear identification
Applied Soft Computing
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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This paper presents a simulation based comparison of Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Least Squares Support Vector Machines (LS-SVM) in parallel mode identification of a chemical process displaying several challenges. The paper provides a graphical analysis of the nonlinear behavior for the system under investigation, a case study of purely parallel identification scheme, the effects of noise in the training data on the prediction performance and the performance comparison of the standard approaches under limited amount of numerical data. The results have shown that the emulators utilizing the MLP structure are superior to the others in terms of predicting the system trajectories, locating the limit cycle, noise driven response and predicting the steady state conditions given only 582 pairs of training data. Furthermore, as opposed to others, with the MLP structure, these qualities disappear smoothly as the noise level is increased gradually.