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The class of Basis Function Piecewise-affine (BPWA) functions is an ideal model structure for nonlinear and hybrid system identification. This paper proposes a parameter space decomposition theorem for BPWA functions. It is proved that any BPWA function has a decomposed parametric representation, in which the algebraic and geometrical parameters can be identified separately. Under the assumption of invariant geometrical structure, a recursive algorithm is proposed to identify the BWPA AutoRegressive Exogenous models from the input-output data. Two benchmark examples are illustrated to show that the proposed algorithm has much higher computational efficiency compared with the competing algorithms with the same identification accuracy.
목차
Abstract
1. Introduction
2. BPWA Functions
2.1 BPWA Basis Function
2.2 BPWA Approximation Theorem
3. Parameter Space Decomposition Theory of BPWA Functions
4. Recursive Identification of PWA Systems
4.1 BPWARX Model [17]
4.2 Change of Regression Vector
4.3 Recursive Identification Algorithm
5. Numerical Examples
5. Conclusions
References
1. Introduction
2. BPWA Functions
2.1 BPWA Basis Function
2.2 BPWA Approximation Theorem
3. Parameter Space Decomposition Theory of BPWA Functions
4. Recursive Identification of PWA Systems
4.1 BPWARX Model [17]
4.2 Change of Regression Vector
4.3 Recursive Identification Algorithm
5. Numerical Examples
5. Conclusions
References
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자료제공 : 네이버학술정보