On L/sup 2/-sensitivity minimization of linear state-space systems

Yan, Wei-Yong; Moore, John B.

doi:10.1109/icsyse.1992.236939

Cited by 16 publications

(39 citation statements)

References 3 publications

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“…Two of them are based on a mixed 1 / 2 norm or a pure 2 norm that measures changes of a certain transfer function, while the other is defined in terms of the poles and zeros of a filter. Several techniques for minimizing the 1 / 2 -sensitivity measure [1]- [5] and the 2 -sensitivity measure [6]- [10] have been proposed. Alternatively, the pole and zero sensitivity of a filter with respect to statespace parameters has been analyzed, and its reduction or minimization have been considered [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Weighted pole and zero sensitivity minimization for state-space digital filters

Hinamoto

Doi

2015

2015 IEEE International Symposium on Circuits and Systems (ISCAS)

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This paper investigates the problem of minimizing weighted pole and zero sensitivity for state-space digital filters. A new measure for pole and zero sensitivity is proposed and an efficient iterative technique for minimizing this measure is developed by employing a quasi-Newton algorithm. A novel and simple method is also explored for obtaining the optimal coordinate transformation matrix which minimizes a zero sensitivity measure subject to minimal pole sensitivity. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.

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Section: Introductionmentioning

confidence: 99%

Weighted pole and zero sensitivity minimization for state-space digital filters

Hinamoto

Doi

2015

2015 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…L 2 -sensitivity is one of the evaluation functions which evaluate the coefficient quantization effects of state-space digital filters [1], [2]. The L 2 -sensitivity minimization is quite beneficial technique for the synthesis of high-accuracy digital filter structures, which achieve quite low-coefficient quantization error.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the L 2 -sensitivity minimization problem has been investigated [1], [2]. To the L 2 -sensitivity minimization problem, Yan et al [1] and Hinamoto et al [2] proposed solutions using iterative calculations.…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of the minimum L<inf>2</inf>-sensitivity realizations of second-order digital filters without iterative calculations

Yamaki

Abe

Kawamata

2010

2010 10th International Symposium on Communications and Information Technologies

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This paper proposes synthesis of the minimum L2-sensitivity realizations of second-order digital filters without iterative calculations. We restrict ourselves to second-order digital filters and consider three cases of second-order digital filters: complex conjugate poles, distinct real poles, and multiple real poles. We can express the L2-sensitivity by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, we achieve the L2-sensitivity minimization without iterative calculations.

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“…In [1]- [12], two main classes of techniques have been explored for constructing state-space descriptions that minimize the coefficient sensitivity: l 1 /l 2 -sensitivity minimization [1]- [6] and l 2 -sensitivity minimization [7]- [12]. It has been argued in [7]- [12] that the sensitivity measure based on the l 2 norm is more natural and reasonable relative to that based on the l 1 /l 2 -sensitivity minimization. Alternatively, it is well known that the use of scaling constraints can be beneficial for suppressing overflow oscillations [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Minimization of frequency-weighted l<inf>2</inf>-sensitivity subject to l<inf>2</inf>-scaling constraints for MIMO linear discrete-time systems using quasi-Newton algorithm

Hinamoto

Tanaka

Doi

2008

2008 4th European Conference on Circuits and Systems for Communications

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The problem of minimizing a frequency-weighted l2-sensitivity measure subject to l2-scaling constraints is considered for multi-input/multi-output (MIMO) linear discrete-time systems. The constrained optimization problem is converted into an unconstrained optimization problem by using linear-algebraic techniques. An efficient quasi-Newton algorithm with closed-form formula for gradient evaluation is then applied to solve the unconstrained optimization problem. Finally, the optimal system structure is constructed by employing the resulting coordinate transformation matrix that minimizes the frequency-weighted l2-sensitivity measure subject to the scaling constraints. A numerical example is also presented to illustrate the utility of the proposed technique.

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On L/sup 2/-sensitivity minimization of linear state-space systems

Cited by 16 publications

References 3 publications

Weighted pole and zero sensitivity minimization for state-space digital filters

Weighted pole and zero sensitivity minimization for state-space digital filters

Synthesis of the minimum L<inf>2</inf>-sensitivity realizations of second-order digital filters without iterative calculations

Minimization of frequency-weighted l<inf>2</inf>-sensitivity subject to l<inf>2</inf>-scaling constraints for MIMO linear discrete-time systems using quasi-Newton algorithm

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