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Bistritz stability criterion : ウィキペディア英語版
Bistritz stability criterion
In signal processing and control theory, the Bistritz criterion is a simple method to determine whether a discrete linear time invariant (LTI) system is stable proposed by Yuval Bistritz.〔Y. Bistritz (1984) ( Zero location with respect to the unit circle of discrete-time linear system polynomials ), Proc. of the IEEE, 72 (9): 1131–1142.〕〔Y. Bistritz (2002) ( Zero location of polynomials with respect to the unit circle unhampered by nonessential singularities ), IEEE Trans. CAS I, 49(3): 305–314.〕 Stability of a discrete LTI system requires that its characteristic polynomials
: D_n(z)=d_0+d_1 z+d_2 z^2+ \cdots + d_z^ + d_n z^n
(obtained from its difference equation, its dynamic matrix, or appearing as the denominator of its transfer function) is a stable polynomial, where D_n(z) is said to be stable if all its zeros are inside the unit circle, viz.
: | z_k | < 1 , k=1,\ldots,n ,
where D_n(z)=d_n \prod_^n (z-z_k) . The test determines whether D_n(z) is stable algebraically (i.e. without numerical determination of the zeros) . The method also solves the full zero location (ZL) problem. Namely, it can count the number of inside the unit-circle (IUC) zeros (~ | z_k| < 1 ~) , on the unit-circle zeros (UC) zeros (~ | z_k| = 1 ~) and outside the unit-circle (OUC) zeros (~ | z_k| > 1 ~) for any real or complex polynomial.〔〔
The Bistritz test is the discrete equivalent of Routh criterion used to test stability of continuous LTI systems. This title was introduced soon after its presentation.〔E. I. Jury and M. Mansour (1985), ( On the terminology relationship between continuous and discrete systems criteria ), Proc. of the IEEE, 73(4):884.〕 It has been also recognized to be more efficient than previously available stability tests for discrete systems like the Schur–Cohn and the Jury test.〔K. Premaratne, and E. I. Jury (1993) (On the Bistritz tabular form and its relationship with the Schur–Cohn minors and inner determinants ), J. of the Franklin Institute, 30(1):165-182.〕
In the following, the focus is only on how to test stability of a real polynomial. However, as long as the basic recursion needed to test stability remains valid, ZL rules are also brought.
==Algorithm==

Consider D_n(z) as above and assume D_n(1) \neq 0 . (If D_n(1)=0 the polynomial is not stable.) Define its reciprocal polynomial
:D^\sharp_n(z)=z^n D_n(1/z)=d_n+d_z+d_ z^2+\cdots+d_z^ + d_0 z^n .
The algorithm assigns to D_n(z) a sequence of symmetric polynomials
:T_m(z)=T^\sharp_m(z), m=n,n-1, \ldots , 0
created by a three-term polynomial recursion. Write out the polynomials by their coefficients,
:T_m(z)=\sum_^m t_ z^k ,
symmetry means that
:T_m(z)=t_+t_ z + \cdots + t_ z^+t_ z^m ,
so that it is enough to calculate for each polynomial only about half of the coefficients. The recursion begins with two initial polynomials driven from the sum and difference of the tested polynomial and its reciprocal, then each subsequent polynomial of reduced degree is produced from the last two known polynomials.
Initiation:
: T_n(z)=D_n(z)+D^\sharp_(z) \quad, \quad T_(z)=\frac
Recursion: For m=n-1,\ldots,1 do:
: \delta_=\frac
: T_(z)=\frac(z) }

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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