From 86b9f7ef7b7dc1df31b0f438c12169cf73856ac9 Mon Sep 17 00:00:00 2001 From: f-cazzola Date: Sat, 6 Jun 2020 23:01:23 +0200 Subject: [PATCH] fix typo --- lectures/2020-04-30.tex | 2 +- lectures/2020-05-05.tex | 2 +- lectures/2020-05-14.tex | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/lectures/2020-04-30.tex b/lectures/2020-04-30.tex index d87289b..2b0afa2 100644 --- a/lectures/2020-04-30.tex +++ b/lectures/2020-04-30.tex @@ -26,7 +26,7 @@ The performance index can be redefined: \[ - \tilde{J}_H (\theta) = \frac{1}{H} \sum_{i=1}^H \gamma_i \left(W(e^{j\omega_i};\theta) - \frac{\hat{B}_i}{A_i}e^{j\hat{\phi_i}}\right)^2 + \tilde{J}_H (\theta) = \frac{1}{H} \sum_{i=1}^H \lambda_i \left(W(e^{j\omega_i};\theta) - \frac{\hat{B}_i}{A_i}e^{j\hat{\phi_i}}\right)^2 \] Another \emph{trick}: more dense $\omega_i$ spacing in the frequency region of special interest (not really used). diff --git a/lectures/2020-05-05.tex b/lectures/2020-05-05.tex index 566013f..1d9fad4 100644 --- a/lectures/2020-05-05.tex +++ b/lectures/2020-05-05.tex @@ -132,7 +132,7 @@ \subsection{Exogenous input} \end{tikzpicture} \end{figure} -Notice that $K(t)$ remains the same because $P(t)$ is the covariance of the prediction error on $x(t)$ and remains the same because $Gu(t)$ introduce any additional noise or uncertainties to the system. +Notice that $K(t)$ remains the same because $P(t)$ is the covariance of the prediction error on $x(t)$ and remains the same because $Gu(t)$ doesn't introduce any additional noise or uncertainties to the system. $Gu(t)$ is a totally known (deterministic) signal. \subsection{Multi-step Prediction} diff --git a/lectures/2020-05-14.tex b/lectures/2020-05-14.tex index ef1a5c5..cfb385f 100644 --- a/lectures/2020-05-14.tex +++ b/lectures/2020-05-14.tex @@ -100,7 +100,7 @@ \section{Non-linear Systems} \begin{figure}[H] \centering \begin{tikzpicture}[node distance=2cm,auto,>=latex'] - \node[int,double border,align=center] at (0,0) (n) {non-lin\\dyn T.I. sys}; + \node[int,dashed border,align=center] at (0,0) (n) {non-lin\\dyn T.I. sys}; \draw[<-,transform canvas={yshift=0.3cm}] (n) -- ++(-2,0) node[left] {$u(t)$}; \draw[<-,transform canvas={yshift=-0.3cm}] (n) -- ++(-2,0) node[left] {$y(t)$}; \draw[->] (n) -- ++(2,0) node[right] {$\hat{x}(t)$}; @@ -308,7 +308,7 @@ \section{Non-linear Systems} \begin{figure}[H] \centering \begin{tikzpicture}[node distance=2cm,auto,>=latex'] - \node[int, dashed border, minimum width=1.5cm, minimum height=3cm] at (0,0) (sys) {}; + \node[int, double border, minimum width=1.5cm, minimum height=3cm] at (0,0) (sys) {}; \node[int, dashed border, minimum height=3cm] at (4,0) (f) {$f(\cdot,\theta)$}; \draw[<-,transform canvas={yshift=0.5cm}] (sys) -- ++(-2cm,0) node[left] {$u(t)$};