2021-10-26数学建模--神经网络在线绘图工具，流程图绘图

作者: keeeeeenon | 来源:发表于2021-10-26 11:14 被阅读0次

一、公式王
网站：https://gongshi.wang/，从此再也不用手敲恶心的数学公式了～
应该是利用OCR识别图片中的公式，再将其转换为latex和mathML格式。其中mathML格式粘贴到word中，选择‘仅保留文本’，可以完美的显示，在Office和wps切换中，也没有出现错误。
在我使用的过程中，反应很迅速，也没有出现错误。
希望大家觉得不错时，可以多给作者打赏！。

二、神经网络画图工具
网址：overloaf在线绘图
使用latex格式绘制神经网络，当然也可以使用latex的tikz进行绘图哈。主界面如下：

我用它在线绘制LSTM的效果图：

绘制LSTM网络的latex代码（代码见水印）：
'''
% 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]{ $\mathbf{u}_{k-1}$ }; &
&
\node (u_k) [input]{ $\mathbf{u}_k$ }; &
&
\node (u_k+1) [input]{ $\mathbf{u}_{k+1}$ }; &
\
% Second line: System noise & input matrix
\node (w_k-1) [noise] { $\mathbf{w}_{k-1}$ }; &
\node (B_k-1) [matrx] { $\mathbf{B}$ }; &
\node (w_k) [noise] { $\mathbf{w}_k$ }; &
\node (B_k) [matrx] { $\mathbf{B}$ }; &
\node (w_k+1) [noise] { $\mathbf{w}_{k+1}$ }; &
\node (B_k+1) [matrx] { $\mathbf{B}$ }; &
\
% Third line: State & state transition matrix
\node (A_k-2) { $\cdots$ }; &
\node (x_k-1) [state] { $\mathbf{x}_{k-1}$ }; &
\node (A_k-1) [matrx] { $\mathbf{A}$ }; &
\node (x_k) [state] { $\mathbf{x}_k$ }; &
\node (A_k) [matrx] { $\mathbf{A}$ }; &
\node (x_k+1) [state] { $\mathbf{x}_{k+1}$ }; &
\node (A_k+1) { $\cdots$ }; \
% Fourth line: Measurement noise & measurement matrix
\node (v_k-1) [noise] { $\mathbf{v}_{k-1}$ }; &
\node (H_k-1) [matrx] { $\mathbf{H}$ }; &
\node (v_k) [noise] { $\mathbf{v}_k$ }; &
\node (H_k) [matrx] { $\mathbf{H}$ }; &
\node (v_k+1) [noise] { $\mathbf{v}_{k+1}$ }; &
\node (H_k+1) [matrx] { $\mathbf{H}$ }; &
\
% Fifth line: Measurement
&
\node (z_k-1) [measurement] { $\mathbf{z}_{k-1}$ }; &
&
\node (z_k) [measurement] { $\mathbf{z}_k$ }; &
&
\node (z_k+1) [measurement] { $\mathbf{z}_{k+1}$ }; &
\
};

% 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 $\mathbf{x}_k$ is propagated to the new state
estimation $\mathbf{x}_{k+1}$ by multiplication with the constant state
transition matrix $\mathbf{A}$ . The state vector $\mathbf{x}_{k+1}$ is
additionally influenced by the control input vector $\mathbf{u}_{k+1}$
multiplied by the input matrix $\mathbf{B}$ , and the system noise vector
$\mathbf{w}_{k+1}$ . The system state cannot be measured directly. The
measurement vector $\mathbf{z}_k$ consists of the information contained
within the state vector $\mathbf{x}_k$ multiplied by the measurement
matrix $\mathbf{H}$ , and the additional measurement noise $\mathbf{v}_k$ .

\end{document}
'''
三、visual-paradigm在线绘图
还有一个在线绘图网站：visual-paradigm

网站有很多好看的模板，可修改性强，主界面：

上面三个就是这次用的很爽的工具了，记录一下😊。
对了，发现一个研究生数学建模论文收集的网址，分享一下：历年研究生数学建模优秀论文汇总
————————————————
版权声明：本文为CSDN博主「慕木子」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/MumuziD/article/details/108709537

2021-10-26数学建模--神经网络在线绘图工具，流程图绘图

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