Model structure. Lecture Note #3 (Chap.6) Identification of time series model. ARMAX Models and Difference Equations

Transcription

1 System Modeling and Identification Lecture ote #3 (Chap.6) CHBE 70 Korea University Prof. Dae Ryoo Yang Model structure ime series Multivariable time series x [ ] x x xm Multidimensional time series (temporal+spatial) Model classification SISO/MIMO Linear/onlinear Deterministic/Stochastic For linear discrete-time systems Difference equation and ARMAX models ransfer function model State-space model - -3 Chap.6 Identification of ime- Series Model Identification of time series model Model structure Parametric estimation Least square method Excellent properties when disturbances are uncorrelated Otherwise, there may be systematic errors and bias More sophisticated methods are needed Handling correlated disturbances Extension of linear regression ARMAX Models and Difference Equations ARMAX (AutoRegressive Moving Average with exogenous input) model A( z ) y z d B( z ) u C( z ) w where d is time delay and A( z ) a z a z an z A nb B( z ) b bz b z bn z B nc Cz ( ) cz cz c z 0 with unnown parameters [ a a n b b n c c n w] ( ) (AR) d nc A B C Az y w y C( z ) w (MA or FIR) Az ( ) y z Bz ( ) u w (ARX) Az ( ) y Cz ( ) w (ARMA) Az y Bz u Cz w ( ) ( ) ( ) (ARIMAX or CARIMA) - Integrated Controlled -4

2 ARMAX: General model ARX: Controlled autoregressive model and Linear regression model when disturbance is measured AR: Model harmonics compounded with noise, and runcated impulse response (FIR) model ARMA: Model-based spectrum analysis ARIMAX: For nonzero means and drift or nonstationary disturbance cases Az y Bz u Cz w ( ) ( ) ( ) A ( z ) y ( ) ( )( ) B z u C z z w Integrated white noise: onstationary (random wal) ransfer Function Models ransfer function models * y H u( z) u H v( z) v ( E{ vv i j} v ij) Bz ( ) Cz ( ) Az ( ) y u w F( z ) D( z ) Bz ( ) y u (Output error (OE) model) v F( z ) Bz ( ) Cz ( ) y u (Box-Jenins (BJ) model) w F( z ) D( z ) OE model: o assumption on disturbance sequence {v } BJ model: Filtered white noise {w } sequence by C/D -5-7 Prediction Error Method (PEM) Methods to predict y based on previous data and the identified model PEM min E( y ( ) y ) min E ( ) It minimizes the variance of the prediction steps ahead of the output y where the prediction is based on the present data. y ( A) y Bu Cw ( Ay ) Bu ( Cw ) w ( A) y Bu ( C)( C ( Ay Bu )) w z w E{( y y ) } E{( z w y ) } E{( z y ) } E{ w} w Minimum attainable variance -6 Difference between output error and prediction error y ay bu (Output error method) y ay bu (Prediction error method) Output error relies more on the accuracy of future output modeling. Prediction error uses actual output. Output error identification is a nonlinear estimation problem. Prediction error identification is a linear estimation problem. Algorithm for OE identification. Least square identification to find initial estimate of F and B. M : F( z ) y B( z ) u v. Filter the data according to F F y y / F ( z ), u u / F ( z ) Prewhitening filter 3. Subsequence estimation of F and B from the model. F F M : F( z ) y B( z ) u v 4. Repeat -3 until the estimate converges. -8

3 Comparison of error models for identification -9 For ARMAX model A( z ) y d z B( z ) u C( z ) w log L( ) 0.5log(( ) det ) 0.5 ( ) ( ) v v y where y ay an y n bu dbu A A dn B Unnown v cv cn CvnC v [ y yn u ] A d udn v B vn C [ a an b b ] A n c c B nc he empirical lielihood function when v = v I ( v unnown) log L(, ) ( / )log( ) ( / ) log( ) (/ ) ( ) v v v ( /)log( ) ( /)log( v) (/ v) V( ) log L(, v) (/ v) V( ) 0 4 log L(, ) (/ ) ( ) ( / ) 0 v v V v v ( ( ( i V ( V ) i i ) i ) i ) v ( V ) 0 (/ V ) ( ) ( ( )) ( ) (ewton-raphson method) - Maximum-Lielihood Method Select estimate so that the observation Y is most probable. max py ( ) py ( ) Lielihood function Example 6.4 Y v ( Ev { } 0 and Evv { } 0) / Assume that pv ( ) (( ) det ) v exp( 0.5 vv v) / Lielihood function: pv ( ) (( ) det v) exp( 0.5( Y) v ( Y)) log L( ) log p( ) 0.5log(( ) det v) 0.5 v If the model is linear in parameters with normally distributed white noise, the maximum lielihood estimate is same as Marov estimate. Cramer-Rao lower bound log L log L log L Cov( ) E E Example 6.5 LS and ML identification S y ay bu w cw : For colored noise, ML identification performs better than LS. LS can estimates only a and b. Local minimum Example 6.6 Pseudolinear regression S: A( z ) y B( z ) u C( z ) w Estimate high order polynomials A and B by least squares he computed residual sequence { } yields a good approximation of white noise sequence {w }. Extend the regressor with { } and then estimate the polynomials of A, B and C using least squares identification. It is also called two-step linear regression. Fisher information matrix -0-3

4 Kalman Filter State-space model x x u v Ev { } 0, Ee { } 0 y Cx Du e E{ vv } R, E{ ee } R, P(0) E{ x0x0} R0 Optimal estimate of x based on the input-output data min J( x) E{( x x ) } Kalman filter (Kalman-Bucy filter) Kalman filter will minimize the above minimization when v and e are independent and normally distributed. x x u ( ) K y Cx K PC ( R CPC ) P P R PC ( R CPC ) CP Cases for time-varying parameters v Ev { } 0, Ee { } 0 y e K ( y ) K P( R P) P P R P ( R P ) P Excellent for time-varying systems R and R are important design parameter that should match the temporal variations of and the observation noise, respectively Derivation he prediction error x x x he prediction error dynamics x ( KCx ) v Ke he mean prediction error E{ x } ( KC) E{ x} he mean square prediction error Ex { x } E{[( KCx ) v Ke ][( KCx ) v Ke ] } ( KCExx ) { }( KC ) v KeK Let P E{ xx } and Q e CPC P P KCP PC K v KQK P P v PC Q CP ( K PC Q ) Q ( K PC Q ) Minimization of P + gives K PC ( e CPC ) P P v PC ( e CPC ) CP (Riccati equation) Instrumental Variable Method Correlation between the regressors and the prediction error leads to bias of the parameter estimates obtained from least-square solutions to the linear regression problem Replace regressor by some other variable Z: IV method In order to mae the estimator consistent EZv { } 0 ran( Z ) p z ( Z ) ZY z z z Cov( ) E{( )( ) } ( Z ) Z vz( Z) he instrumental variable abeshould oudbec chosen sothat they eyae are simultaneously uncorrelated with v and highly correlated with

5 Example 6.8 S: y 0.9y 0.uw 0.7w ( Ew { } 0, Ew { } w) Biased least-square estimate of parameters y u [ ab] ( ) Y[ ] Instrumental variable y u z az bu z u Z z ( Z ) Z Y [ ] z u Shows reduced bias 0 Example 6.9 u0 u For a choice of IV Z u u z ( Z ) Z Y [ ] 047] Gives very poor estimate. It might be difficult to choose appropriate instrumental variables. hus, an iterative procedure are usually used. -7 Some Aspects of Application Prefiltering, smoothing, prewhitening f f Y ( z) F( z ) Y( z), U ( z) F( z ) U( z) f f M : Az ( ) y Bz ( ) u ( v For periodic variation, use F(z 0 w) )= z d when d is the period of trend. Bias reduction rend elimination y ( y )/, u ( u )/ M : Az ( )( y y) Bz ( )( u u) v Differentiation i i of data M : Az ( ) y Bz ( ) u v M : Az ( ) y Bz ( ) u v It gives improved accuracy Offset estimation via an extra parameter M : Az ( ) y Bz ( ) u ( v w) 0 It introduces new noise correlation Extra parameter -9 Example 6.0 (he Yule-Waler equations) Consider the AR process S: A( z ) y w ; ( E{ w } 0, E{ w } ) w * Cyy ( ) E{ y y } E{( ai y i w ) y } ae i { y i y } E{ w y } i i ac ( ), 0 i yy i i w Cyy ( ) ac ( ), 0 i i yy i Choosing numbers M> n A and p>n A and [ y yn ] A z [ y yp]/ M ( p,, M p) Cyy ( i) Cyy ( i) Cyy ( i) a Cyy ( i) Cyy () i Cyy ( i) Cyy ( i ) a Cyy ( i ) Cyy ( i p) Cyy ( i p) Cyy ( i p ) an C ( ) A yy i p z ( Z ) ZY ( Z ) ( Z Y ) -8 Convergence and Consistency Convergence in L p p,, 0<p< p p lim E{ x x } 0 Convergence almost surely lim P{ x x, n, 0} n Convergence in probability P{ x x, 0} 0 Central limit theorem Let {x } be a sequence of independent random variables with common distribution function F with finite mean and variance. X has a limiting normal distribution with mean 0 and variance as. S dist If S x, then X ormal(0,) -0 5

6 Efficient estimate, E{( ) } E{( ) } for any other estimate Consistent estimate Probability limit lim E{( ) } 0 lim P {, 0} 0 plim Unbiased and asymptotically unbiased estimates E{ } (Unbiased estimate) lim E{ } (Asymptotically unbiased estimate) - 6