The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Nonlinear control systems: an introduction (2nd ed.)
Nonlinear control systems: an introduction (2nd ed.)
Analysis of neural excitability and oscillations
Methods in neuronal modeling
Symbolic calculation of zero dynamics for nonlinear control systems
ISSAC '91 Proceedings of the 1991 international symposium on Symbolic and algebraic computation
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
System analysis and signal processing: with emphasis on the use of MATLAB
System analysis and signal processing: with emphasis on the use of MATLAB
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
The Analysis and Design of Linear Circuits
The Analysis and Design of Linear Circuits
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We consider the simultaneous reconstruction of the state vector and unknown parameters for a special class of non-linear parameter-dependent systems. The approach suggested here consists of two stages. First, the state is reconstructed by a reduced order unknown input observer. Second, this observer is augmented by a filter to obtain the desired parameter. The design procedure is straightforward and may be used for an online estimation of the unknown parameter. In contrast to previous work on adaptive systems, we do not require a persistence of excitation. The approach is illustrated on two non-linear cell models.