Neural networks for control
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
The evolutionary learning rule for system identification
Applied Soft Computing
An orthogonal neural network for function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Adaptive output feedback tracking control of robot manipulators using position measurements only
Expert Systems with Applications: An International Journal
Online hybrid traffic classifier for Peer-to-Peer systems based on network processors
Applied Soft Computing
Non-linear dynamic system identification using Cascaded Functional Link Artificial Neural Network
International Journal of Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Real-time implementation of Chebyshev neural network observer for twin rotor control system
Expert Systems with Applications: An International Journal
A novel learning scheme for Chebyshev functional link neural networks
Advances in Artificial Neural Systems
Expert Systems with Applications: An International Journal
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This paper proposes a computationally efficient artificial neural network (ANN) model for system identification of unknown dynamic nonlinear discrete time systems. A single layer functional link ANN is used for the model where the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. Thus, creation of nonlinear decision boundaries in the multidimensional input space and approximation of complex nonlinear systems becomes easier. These models are linear in their parameters and nonlinear in the inputs. The recursive least squares method with forgetting factor is used as on-line learning algorithm for parameter updation. The good behaviour of the identification method is tested on Box and Jenkins Gas furnace benchmark identification problem, single input single output (SISO) and multi input multi output (MIMO) discrete time plants. Stability of the identification scheme is also addressed.