★ FEATURED ARTICLE 欢迎来到海神之光博客之家✅博主简介热爱科研的Matlab仿真开发者修心和技术同步精进个人主页海神之光代码获取方式海神之光Matlab王者学习之路—代码获取方式⛳️座右铭行百里者半于九十。更多Matlab图像处理仿真内容点击①Matlab图像处理进阶版②付费专栏Matlab图像处理初级版⛳️关注CSDN海神之光更多资源等你来⛄一、 KNN算法简介K最近邻(k-Nearest NeighborKNN)分类算法是一个理论上比较成熟的方法也是最简单的机器学习算法之一。该方法的思路是如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别则该样本也属于这个类别。1 定义如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别则该样本也属于这个类别即由你的“邻居”来推断出你的类别.2 距离公式两个样本的距离可以通过如下公式计算又叫欧式距离3 KNN算法的步骤1计算已知类别数据集中每个点与当前点的距离2选取与当前点距离最小的K个点3统计前K个点中每个类别的样本出现的频率4返回前K个点出现频率最高的类别作为当前点的预测分类。4 KNN原理5 KNN的优缺点6 KNN性能问题NN的性能问题也是KNN的缺点之一。使用KNN可以很容易的构造模型但在对待分类样本进行分类时为了获得K近邻必须采用暴力搜索的方式扫描全部训练样本并计算其与待分类样本之间的距离系统开销很大。⛄二、部分源代码function varargout gui_class_run(varargin)% GUI_CLASS_RUN M-file for gui_class_run.fig% GUI_CLASS_RUN, by itself, creates a new GUI_CLASS_RUN or raises the existing% singleton*.%% H GUI_CLASS_RUN returns the handle to a new GUI_CLASS_RUN or the handle to% the existing singleton*.%% GUI_CLASS_RUN(‘CALLBACK’,hObject,eventData,handles,…) calls the local% function named CALLBACK in GUI_CLASS_RUN.M with the given input arguments.%% GUI_CLASS_RUN(‘Property’,‘Value’,…) creates a new GUI_CLASS_RUN or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before gui_class_run_OpeningFcn gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to gui_class_run_OpeningFcn via varargin.%% *See GUI Options on GUIDE’s Tools menu. Choose “GUI allows only one% instance to run (singleton)”.%% See also: GUIDE, GUIDATA, GUIHANDLES% Edit the above text to modify the response to help gui_class_run% Last Modified by GUIDE v2.5 23-Feb-2021 16:38:14% Begin initialization code - DO NOT EDITgui_Singleton 1;gui_State struct(‘gui_Name’, mfilename, …‘gui_Singleton’, gui_Singleton, …‘gui_OpeningFcn’, gui_class_run_OpeningFcn, …‘gui_OutputFcn’, gui_class_run_OutputFcn, …‘gui_LayoutFcn’, [] , …‘gui_Callback’, []);if nargin ischar(varargin{1})gui_State.gui_Callback str2func(varargin{1});endif nargout[varargout{1:nargout}] gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% — Executes just before gui_class_run is made visible.function gui_class_run_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% varargin command line arguments to gui_class_run (see VARARGIN)% Choose default command line output for gui_class_runhandles.output hObject;% Update handles structureguidata(hObject, handles);% UIWAIT makes gui_class_run wait for user response (see UIRESUME)% uiwait(handles.figure1);set(handles.pushbutton4,‘Visible’,‘off’);set(handles.pushbutton5,‘Visible’,‘off’);set(handles.axes1,‘Visible’,‘off’);set(handles.axes2,‘Visible’,‘off’);set(handles.axes3,‘Visible’,‘off’);set(handles.axes4,‘Visible’,‘off’);set(handles.axes5,‘Visible’,‘off’);set(handles.axes6,‘Visible’,‘off’);set(handles.axes7,‘Visible’,‘off’);set(handles.axes8,‘Visible’,‘off’);set(handles.axes9,‘Visible’,‘off’);set(handles.axes10,‘Visible’,‘off’);% — Outputs from this function are returned to the command line.function varargout gui_class_run_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} handles.output;% — Executes on button press in pushbutton1.function pushbutton1_Callback(hObject, eventdata, handles)% hObject handle to pushbutton1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)set(handles.pushbutton2,‘Visible’,‘off’);set(handles.pushbutton4,‘Visible’,‘on’);set(handles.pushbutton5,‘Visible’,‘off’);set(handles.axes1,‘Visible’,‘on’);set(handles.axes2,‘Visible’,‘on’);set(handles.axes3,‘Visible’,‘on’);set(handles.axes4,‘Visible’,‘on’);set(handles.axes5,‘Visible’,‘on’);set(handles.axes6,‘Visible’,‘on’);set(handles.axes7,‘Visible’,‘on’);set(handles.axes8,‘Visible’,‘on’);set(handles.axes9,‘Visible’,‘on’);set(handles.axes10,‘Visible’,‘on’);pause(1);[J P]uigetfile(‘.’,‘select the Query Image’);Iimread(strcat(P,J));Iimresize(I,[256 256]);axes(handles.axes1);imshow(I);title(‘Query Image’);Iim2double(I);k1;wn{‘haar’,‘db2’,‘db4’,‘dmey’,‘bior1.1’};for w1:5 % three waveletsfor c1:3 % three band of colorsTI(:,:,c);for i1:6 % six decomposition levels[a1,b1,c1,d1]dwt2(T,wn{w});fq(k,:)my_feature(a1); % extraction of featuresTa1;kk1;endendendhandles.fqfq;% Update handles structureguidata(hObject, handles);% — Executes on button press in pushbutton2.function pushbutton2_Callback(hObject, eventdata, handles)% hObject handle to pushbutton2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)set(handles.pushbutton1,‘Visible’,‘off’);set(handles.pushbutton5,‘Visible’,‘on’);[J P]uigetfile(‘.’,‘select the Query Image’);Iimread(strcat(P,J));Iimresize(I,[256 256]);Iim2double(I);k1;wn{‘haar’,‘db2’,‘db4’,‘dmey’,‘bior1.1’};for w1:5 % three waveletsfor c1:3 % three band of colorsTI(:,:,c);for i1:6 % six decomposition levels[a1,b1,c1,d1]dwt2(T,wn{w});fq(k,:)my_feature(a1); % extraction of featuresTa1;kk1;endendendset(handles.axes1,‘Visible’,‘on’);axes(handles.axes1);imshow(I);title(‘Query Image’);% — Executes on button press in pushbutton3.handles.fqfq;guidata(hObject, handles);% — Executes on button press in pushbutton4.function pushbutton4_Callback(hObject, eventdata, handles)% hObject handle to pushbutton4 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)Fdir(‘database’);Fchar(F.name);szsize(F,1)-3;hwaitbar(0,‘Please wait the system is searching’ );g[];for i1:4g[g repmat([i],1,10)];endfor ff1:szstrstrcat(num2str(ff),‘.jpg’);cd databaseIimread(str);cd …Iimresize(I,[256 256]);Iim2double(I);k1;wn{‘haar’,‘db2’,‘db4’,‘dmey’,‘bior1.1’};for w1:5 % three waveletsfor c1:3 % three band of colorsTI(:,:,c);for i1:6 % six decomposition levels[a1,b1,c1,d1]dwt2(T,wn{w});fv1(k,:)my_feature(a1); % extraction of featuresTa1;kk1;endendendFT{ff}fv1;waitbar(ff/sz);endclose(h);fqhandles.fq;for ii1:length(FT)ftFT{ii};D(ii)sum(sum(dist(ft(‘,fq()))/numel(I);endDDsort(D);for i1:10dxfind(DD(i)D);strstrcat(num2str(dx),’.jpg’);cd databaseJJ{i}imread(str);cd …endaxes(handles.axes2);imshow(JJ{2});axes(handles.axes3);imshow(JJ{3});axes(handles.axes4);imshow(JJ{4});axes(handles.axes5);imshow(JJ{5});axes(handles.axes6);imshow(JJ{6});axes(handles.axes7);imshow(JJ{7});axes(handles.axes8);imshow(JJ{8});axes(handles.axes9);imshow(JJ{9});axes(handles.axes10);imshow(JJ{10});% — Executes on button press in pushbutton5.function pushbutton5_Callback(hObject, eventdata, handles)% hObject handle to pushbutton5 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)fqhandles.fq;Fdir(‘database’);Fchar(F.name);szsize(F,1)-3;hwaitbar(0,‘Please wait the system is searching’ );g[];for i1:4g[g repmat([i],1,10)];end⛄三、运行结果⛄四、matlab版本及参考文献1 matlab版本2014a2 参考文献[1]谭立球,谷士文,夏胜平.基于RSOM和KNN的图像内容检索[J].微电子学与计算机. 2007,(10)3 备注简介此部分摘自互联网仅供参考若侵权联系删除 仿真咨询1 各类智能优化算法改进及应用生产调度、经济调度、装配线调度、充电优化、车间调度、发车优化、水库调度、三维装箱、物流选址、货位优化、公交排班优化、充电桩布局优化、车间布局优化、集装箱船配载优化、水泵组合优化、解医疗资源分配优化、设施布局优化、可视域基站和无人机选址优化2 机器学习和深度学习方面卷积神经网络CNN、LSTM、支持向量机SVM、最小二乘支持向量机LSSVM、极限学习机ELM、核极限学习机KELM、BP、RBF、宽度学习、DBN、RF、RBF、DELM、XGBOOST、TCN实现风电预测、光伏预测、电池寿命预测、辐射源识别、交通流预测、负荷预测、股价预测、PM2.5浓度预测、电池健康状态预测、水体光学参数反演、NLOS信号识别、地铁停车精准预测、变压器故障诊断3 图像处理方面图像识别、图像分割、图像检测、图像隐藏、图像配准、图像拼接、图像融合、图像增强、图像压缩感知4 路径规划方面旅行商问题TSP、车辆路径问题VRP、MVRP、CVRP、VRPTW等、无人机三维路径规划、无人机协同、无人机编队、机器人路径规划、栅格地图路径规划、多式联运运输问题、车辆协同无人机路径规划、天线线性阵列分布优化、车间布局优化5 无人机应用方面无人机路径规划、无人机控制、无人机编队、无人机协同、无人机任务分配6 无线传感器定位及布局方面传感器部署优化、通信协议优化、路由优化、目标定位优化、Dv-Hop定位优化、Leach协议优化、WSN覆盖优化、组播优化、RSSI定位优化7 信号处理方面信号识别、信号加密、信号去噪、信号增强、雷达信号处理、信号水印嵌入提取、肌电信号、脑电信号、信号配时优化8 电力系统方面微电网优化、无功优化、配电网重构、储能配置9 元胞自动机方面交通流 人群疏散 病毒扩散 晶体生长10 雷达方面卡尔曼滤波跟踪、航迹关联、航迹融合
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