一蟹肘、公式王
網(wǎng)站：https://gongshi.wang/词疼，從此再也不用手敲惡心的數(shù)學(xué)公式了～
應(yīng)該是利用OCR識別圖片中的公式，再將其轉(zhuǎn)換為latex和mathML格式帘腹。其中mathML格式粘貼到word中贰盗，選擇‘僅保留文本’，可以完美的顯示阳欲，在Office和wps切換中舵盈，也沒有出現(xiàn)錯誤陋率。
在我使用的過程中，反應(yīng)很迅速秽晚，也沒有出現(xiàn)錯誤瓦糟。
希望大家覺得不錯時，可以多給作者打賞爆惧！狸页。

二、神經(jīng)網(wǎng)絡(luò)畫圖工具
網(wǎng)址：overloaf在線繪圖
使用latex格式繪制神經(jīng)網(wǎng)絡(luò)扯再，當(dāng)然也可以使用latex的tikz進(jìn)行繪圖哈。主界面如下：

我用它在線繪制LSTM的效果圖：

繪制LSTM網(wǎng)絡(luò)的latex代碼（代碼見水又酚觥）：
'''
% Kalman filter system model
% by Burkart Lingner
% An example using TikZ/PGF 2.00
%
% Features: Decorations, Fit, Layers, Matrices, Styles
% Tags: Block diagrams, Diagrams
% Technical area: Electrical engineering

\documentclass[a4paper,10pt]{article}

\usepackage[english]{babel}
\usepackage[T1]{fontenc}
\usepackage[ansinew]{inputenc}

\usepackage{lmodern} % font definition
\usepackage{amsmath} % math fonts
\usepackage{amsthm}
\usepackage{amsfonts}

\usepackage{tikz}

%%%<
\usepackage{verbatim}
\usepackage[active,tightpage]{preview}
\PreviewEnvironment{tikzpicture}
\setlength\PreviewBorder{5pt}%
%%%>

\begin{comment}
:Title: Kalman Filter System Model
:Slug: kalman-filter
:Author: Burkart Lingner

This is the system model of the (linear) Kalman filter.

\end{comment}

\usetikzlibrary{decorations.pathmorphing} % noisy shapes
\usetikzlibrary{fit} % fitting shapes to coordinates
\usetikzlibrary{backgrounds} % drawing the background after the foreground

\begin{document}

\begin{figure}[htbp]
\centering
% The state vector is represented by a blue circle.
% "minimum size" makes sure all circles have the same size
% independently of their contents.
\tikzstyle{state}=[circle,
thick,
minimum size=1.2cm,
draw=blue!80,
fill=blue!20]

% The measurement vector is represented by an orange circle.
\tikzstyle{measurement}=[circle,
thick,
minimum size=1.2cm,
draw=orange!80,
fill=orange!25]

% The control input vector is represented by a purple circle.
\tikzstyle{input}=[circle,
thick,
minimum size=1.2cm,
draw=purple!80,
fill=purple!20]

% The input, state transition, and measurement matrices
% are represented by gray squares.
% They have a smaller minimal size for aesthetic reasons.
\tikzstyle{matrx}=[rectangle,
thick,
minimum size=1cm,
draw=gray!80,
fill=gray!20]

% The system and measurement noise are represented by yellow
% circles with a "noisy" uneven circumference.
% This requires the TikZ library "decorations.pathmorphing".
\tikzstyle{noise}=[circle,
thick,
minimum size=1.2cm,
draw=yellow!85!black,
fill=yellow!40,
decorate,
decoration={random steps,
segment length=2pt,
amplitude=2pt}]

% Everything is drawn on underlying gray rectangles with
% rounded corners.
\tikzstyle{background}=[rectangle,
fill=gray!10,
inner sep=0.2cm,
rounded corners=5mm]

\begin{tikzpicture}[>=latex,text height=1.5ex,text depth=0.25ex]
% "text height" and "text depth" are required to vertically
% align the labels with and without indices.

% The various elements are conveniently placed using a matrix:
\matrix[row sep=0.5cm,column sep=0.5cm] {
% First line: Control input
&
\node (u_k-1) [input]{}; &
&
\node (u_k) [input]{}; &
&
\node (u_k+1) [input]{}; &
\
% Second line: System noise & input matrix
\node (w_k-1) [noise] {}; &
\node (B_k-1) [matrx] {}; &
\node (w_k) [noise] {}; &
\node (B_k) [matrx] {}; &
\node (w_k+1) [noise] {}; &
\node (B_k+1) [matrx] {}; &
\
% Third line: State & state transition matrix
\node (A_k-2) {}; &
\node (x_k-1) [state] {}; &
\node (A_k-1) [matrx] {}; &
\node (x_k) [state] {}; &
\node (A_k) [matrx] {}; &
\node (x_k+1) [state] {}; &
\node (A_k+1) {}; \
% Fourth line: Measurement noise & measurement matrix
\node (v_k-1) [noise] {}; &
\node (H_k-1) [matrx] {}; &
\node (v_k) [noise] {}; &
\node (H_k) [matrx] {}; &
\node (v_k+1) [noise] {}; &
\node (H_k+1) [matrx] {}; &
\
% Fifth line: Measurement
&
\node (z_k-1) [measurement] {}; &
&
\node (z_k) [measurement] {}; &
&
\node (z_k+1) [measurement] {}; &
\
};

% The diagram elements are now connected through arrows:
\path[->]
    (A_k-2) edge[thick] (x_k-1) % The main path between the
    (x_k-1) edge[thick] (A_k-1) % states via the state
    (A_k-1) edge[thick] (x_k)       % transition matrices is
    (x_k)   edge[thick] (A_k)       % accentuated.
    (A_k)   edge[thick] (x_k+1) % x -> A -> x -> A -> ...
    (x_k+1) edge[thick] (A_k+1)
    
    (x_k-1) edge (H_k-1)                % Output path x -> H -> z
    (H_k-1) edge (z_k-1)
    (x_k)   edge (H_k)
    (H_k)   edge (z_k)
    (x_k+1) edge (H_k+1)
    (H_k+1) edge (z_k+1)
    
    (v_k-1) edge (z_k-1)                % Output noise v -> z
    (v_k)   edge (z_k)
    (v_k+1) edge (z_k+1)
    
    (w_k-1) edge (x_k-1)                % System noise w -> x
    (w_k)   edge (x_k)
    (w_k+1) edge (x_k+1)
    
    (u_k-1) edge (B_k-1)                % Input path u -> B -> x
    (B_k-1) edge (x_k-1)
    (u_k)   edge (B_k)
    (B_k)   edge (x_k)
    (u_k+1) edge (B_k+1)
    (B_k+1) edge (x_k+1)
    ;

% Now that the diagram has been drawn, background rectangles
% can be fitted to its elements. This requires the TikZ
% libraries "fit" and "background".
% Control input and measurement are labeled. These labels have
% not been translated to English as "Measurement" instead of
% "Messung" would not look good due to it being too long a word.
\begin{pgfonlayer}{background}
    \node [background,
                fit=(u_k-1) (u_k+1),
                label=left:Entrance:] {};
    \node [background,
                fit=(w_k-1) (v_k-1) (A_k+1)] {};
    \node [background,
                fit=(z_k-1) (z_k+1),
                label=left:Measure:] {};
\end{pgfonlayer}

\end{tikzpicture}

\caption{Kalman filter system model}
\end{figure}

This is the system model of the (linear) Kalman filter. At each time
step the state vector is propagated to the new state
estimation by multiplication with the constant state
transition matrix . The state vector is
additionally influenced by the control input vector
multiplied by the input matrix , and the system noise vector
. The system state cannot be measured directly. The
measurement vector consists of the information contained
within the state vector multiplied by the measurement
matrix , and the additional measurement noise .

\end{document}
'''
三熄阻、visual-paradigm在線繪圖
還有一個在線繪圖網(wǎng)站：visual-paradigm

網(wǎng)站有很多好看的模板，可修改性強(qiáng)倔约，主界面：

上面三個就是這次用的很爽的工具了秃殉，記錄一下??。
對了浸剩，發(fā)現(xiàn)一個研究生數(shù)學(xué)建模論文收集的網(wǎng)址钾军，分享一下：歷年研究生數(shù)學(xué)建模優(yōu)秀論文匯總
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版權(quán)聲明：本文為CSDN博主「慕木子」的原創(chuàng)文章，遵循CC 4.0 BY-SA版權(quán)協(xié)議绢要，轉(zhuǎn)載請附上原文出處鏈接及本聲明吏恭。
原文鏈接：https://blog.csdn.net/MumuziD/article/details/108709537