1 | V t� kapitole je pops�jednoduch�� na kter�jsou porovn� ��lgoritmy uveden� p�l�apitole. Syst�byl podrobn�koum�v \cite{astrom1986dual}. Pro srovn� uv�me tam���y. |
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2 | |
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3 | \section{Popis syst�} |
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4 | V�syst� je pops�jako |
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5 | \begin{gather} |
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6 | \label{simple} |
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7 | y_{t+1}=y_t+\theta_tu_t+v_{t+1} \qquad t=0,\ldots,N-1,\\ |
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8 | v_t\sim N(0,\sigma^2).\\ |
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9 | \theta_t\sim N(\hat{\theta},P_t),\\ |
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10 | \cov(v_{t+1},\theta)=0. |
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11 | \end{gather} |
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12 | |
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13 | Ztr�vou funkci vol� kvadratickou, tedy |
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14 | \begin{equation} |
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15 | g(y_{0:N},u_{0:N-1},v_{0:N-1})=\sum_{t=0}^{N-1}y_{t+1}^2. |
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16 | \end{equation} |
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17 | |
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18 | Odhadovac�rocedurou pro parametr $\theta$ je Kalman�ltr. Pro syst�\eqref{simple} m�var |
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19 | \begin{gather} |
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20 | \label{kal} |
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21 | K_t=\frac{u_tP_t}{u_t^2P_t+\sigma^2}\\ |
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22 | \hat{\theta}_{t+1}=\hat{\theta}_t+K_t(y_{t+1}-u_t\hat{\theta}_t),\\ |
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23 | P_{t+1}=(1-K_tu_t)P_t. |
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24 | \end{gather} |
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25 | |
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26 | O��n�tr� je |
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27 | \begin{equation} |
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28 | J_t(y_t,\theta_t)=\min_{u_t \in U_t}\E_{y_{t+1},v_t}\left\{y_{t+1}^2+J_{t+1}(y_{t+1},\theta_{t+1})|y_t,\theta_t,u_t\right\}, \qquad t=0,\ldots,N-1. |
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29 | \end{equation} |
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30 | |
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31 | Ta po dosazen� \eqref{simple} a �te�m proveden�t� hodnoty p� na tvar |
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32 | \begin{gather} |
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33 | \label{dos} |
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34 | J_t(y_t,\theta_t)=\min_{u_t \in U_t}\left\{(y_t+\hat{\theta}_tu_t)^2+u_t^2P_t+\sigma^2+\E_{y_{t+1},v_t}(J_{t+1}(y_{t+1},\theta_{t+1}))|y_t,\theta_t,u_t\right\}. |
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35 | \end{gather} |
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36 | |
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37 | ZDE BY MEL BYT ANGSTROM+... |
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38 | |
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39 | \section{Specifika jednotliv��up� tomto odd� jsou pops� n�er�spekty algoritm�er�udeme srovn�t, p�likaci na syst�\eqref{simple}. |
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40 | |
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41 | \subsection{Certainty equivalent control} |
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42 | O��n�tr� \eqref{CE} prejde v |
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43 | \begin{gather} |
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44 | J_t(y_t, \theta_t)=\min_{u_t \in U_t}\left\{\hat{y}_{t+1}^2 +J_{t+1}(y_{t+1},\theta_{t+1})|I_t,\theta_t,u_t\right\}. |
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45 | \end{gather} |
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46 | St� hodnota v� je |
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47 | \begin{equation} |
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48 | \hat{y}_{t+1}=y_t+\hat{\theta}_tu_t |
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49 | \end{equation} |
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50 | a rozhodnut�ude tedy |
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51 | \begin{equation} |
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52 | \mu_t(y_t,\hat{\theta}_t)=-\frac{y_t}{\hat{\theta}_t}. |
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53 | \end{equation} |
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54 | |
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55 | \subsection{Metoda separace} |
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56 | V prvn�� metody separace polo�� ���h |
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57 | \begin{equation} |
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58 | u_0=\sqrt{C-\frac{1}{P_0}}. |
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59 | \end{equation} |
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60 | T�se dle \eqref{kal} sn� rozptyl $P_0$ nezn�ho parametru $\theta$ na $\frac{1}{C}$. Konstanta $C$ by m� b�ena dostate� mal�aby odhad $\hat{\theta}$ pro druhou f� ��yl dostate� bl�o skute� hodnot�arametru $\theta$. P�ovn� jednotliv�goritm�l�me $C=100$. |
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61 | |
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62 | \subsection{SIDP} |
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63 | Dle \eqref{dos} je optim��u_t$ z�sl�a $(y_t,\hat{\theta}_t,P_t)$. P�mulaci m� tedy v ka�d��ov�okam�iku $t$ diskretizovat t�enzion��rostor nez�sle prom��le \cite{astrom1986dual} je v�ak p�amotnou simulac�hodn�� k transformaci prostoru $(y_t,\hat{\theta}_t,P_t,u_t)$ do nov�om��\eta_t,\beta_t,\zeta_t,\nu_t)$ dle |
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64 | \begin{gather} |
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65 | \eta_t=\frac{y_t}{\sigma} \\ |
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66 | \beta_t=\frac{\hat{\theta}_t}{\sqrt{P_t}} \\ |
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67 | \zeta_t=\frac{1}{\sqrt{P_t}} \\ |
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68 | \nu_t=\frac{u_t\sqrt{P_t}}{\sigma} |
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69 | \end{gather} |
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70 | |
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71 | Sou�n�� neur�ost ve v� \eqref{simple} reprezentovat jedinou normalizovanou n�dnou veli�ou podle |
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72 | \begin{equation} |
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73 | s_t=\frac{y_{t+1}-y_t+\hat{\theta}_tu_t}{\sqrt{u_t^2P_t+\sigma^2}} \sim N(0,1). |
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74 | \end{equation} |
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75 | |
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76 | Rovnice pro v�\eqref{simple} a n�eduj� odhad nezn�ho parametru \eqref{kal} tak p� v |
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77 | \begin{gather} |
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78 | \eta_{t+1}=\eta_t+\beta_t\nu_t+\sqrt{1+\nu^2}s_t\\ |
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79 | \beta_{t+1}=\sqrt{1+\nu^2}\beta_t+\nu_ts_t |
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80 | \end{gather} |
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81 | |
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82 | P�me-li k vhodn�praven���n�tr�, dostaneme |
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83 | \begin{align} |
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84 | V_t(\eta_t,\beta_t,\zeta_t)&=\frac{J_t(y_t,\hat{\theta}_t,P_t)}{\sigma^2}\\ |
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85 | &=\min_{\nu_t }\left\{(\eta_t+\beta_t\nu_t)^2+\nu_t^2+1+\E_{y_{t+1},v_t}(V_{t+1}(\eta_{t+1},\beta_{t+1},\zeta))\right\}. |
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86 | \end{align} |
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87 | |
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88 | Nyn�po�me o��nou ztr� pro $N-1$. |
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89 | \begin{equation} |
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90 | V_{N-1}(\eta_{N-1},\beta_{N-1},\zeta_{N-1})=\min_{\nu_{N-1}}\left\{(\eta_{N-1}+\beta_{N-1}\nu_{N-1})^2+\nu_{N-1}^2+1\right\}. |
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91 | \end{equation} |
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92 | |
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93 | Derivac��� optim���h jako |
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94 | \begin{equation} |
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95 | \label{optcon} |
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96 | \nu_{N-1}=-\frac{\eta_{N-1}\beta_{N-1}}{1+\beta_{N-1}^2} |
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97 | \end{equation} |
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98 | a o��nou ztr� |
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99 | \begin{equation} |
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100 | V_{N-1}(\eta_{N-1},\beta_{N-1},\zeta_{N-1})= \frac{\eta_{N-1}^2+1}{\beta_{N-1}^2+1} |
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101 | \end{equation} |
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102 | |
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103 | Proto�e optim���h $\nu_{N-1}$ ani o��n�tr� $V_{N-1}$ nez�s�a $\zeta_{N-1}$, d� tvaru $V_t$ nebude rovn�optim���h $\nu_t$ a o��n�tr� $V_t$ z�set na $\zeta_t$. P�skretizaci tedy sta�uva�ovat pouze dvoudimenzion��rostor nez�sle prom��\eta_t,\beta_t)$. |
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104 | |
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105 | \section{Srovn� jednotliv��up� t� sekci jsou porovn� popsan��c�lgoritmy na syst� \eqref{simple}. |
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106 | POPIS EXPERIMENTU |
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