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Matlab毕设Matlab毕设系列–说明期刊发表发表北大核心SCI不是梦⛳️座右铭行百里者半于九十。更多Matlab优化求解仿真内容点击①Matlab优化求解 进阶版②付费专栏Matlab优化求解初级版⛳️关注CSDN海神之光更多资源等你来⛄一、梯度的方法和遗传算法焊接梁结构优化⛄二、部分源代码和运行步骤1 部分代码% script geralclear;clc;clear all;N_pop 30;P_elite 0.3; % probabilidade de ter eliteN_elite round(P_elite * N_pop,0); % quantidade de membros da eliteP_mut 0.008; % P_muta??o de valor intermédioP_cross 0.9; % Probabilidade de ocorrer crossover%%% variaveis do penalty functionC 0.5;alfa 2;beta 2;%%%% Caracteristicas %%%% Defina as constantes do problemaP 6000; % lbL 14; % inE 30e6; % psiG 12e6; % psitau_max 13600; % psisigma_max 30000; % psidelta_max 0.25; % inparams [P,L,E,G,tau_max,sigma_max,delta_max];%%%% Bounds %%%%lower [0.125; 0.1; 0.1; 0.1];upper [2.0; 10.0; 10.0; 2.0];%%%% Obter bits para cada xi %%%%casas_decimais 3;n_bits zeros(4,1);for i 1:4n_bits(i) ceil(log2((upper(i) - lower(i)) * 10^casas_decimais 1));endn_total_bits sum(n_bits);%% ---------------- Experimentos (sem guardar ficheiros) ----------------N_runs 25; % n? de execu??es (ajusta)base_seed 12345; % seed base para reprodutibilidadet_run zeros(N_runs, 1);results repmat(struct(), N_runs, 1);for r 1:N_runsrng(base_seed r, ‘twister’); % sementes diferentes mas controladas% rng(‘shuffle’);t_start tic;% ---- Executa o GA ----[melhor_indiv, best_hist, pior_el_hist, k_stop, motivo_paragem, pen_hist, lambda_hist, best_real_hist, best_f_hist, std_hist, entropia_x, pop_checkpoints, k_checkpoints, elite_checkpoints] genetic_algorithm( …params, N_pop, N_elite, P_mut, P_cross, n_bits, n_total_bits, upper, lower, C, alfa, beta);t_run® toc(t_start); % guarda o tempo desta run% ---- Métricas finais ---- best_merit_final best_hist(end); pior_el_merit_final pior_el_hist(end); % ---- Valor objetivo (sem penaliza??o) do melhor indivíduo ---- % (usa row vector se a tua funcao_objetivo preferir) best_obj_final funcao_objetivo(melhor_indiv(:).); viavel_final verificar_restri(melhor_indiv, params); % ---- Calcular restri??es violadas ---- resti calcular_restricoes(params, melhor_indiv); violadas resti 0; num_violadas nnz(violadas); if num_violadas 0 max_violacao max(resti(violadas)); else max_violacao 0; end % ---- Guardar em memória (sem ficheiros) ---- results(r).run_id r; results(r).seed base_seed r; results(r).best_hist best_hist; results(r).pior_el_hist pior_el_hist; results(r).pen_hist pen_hist; results(r).lambda_hist lambda_hist; results(r).best_real_hist best_real_hist; % variáveis (x1,x2,x3,x4) results(r).best_f_hist best_f_hist; % f(x) do melhor results(r).std_hist std_hist; % desvio padr?o das variáveis results(r).pop_checkpoints pop_checkpoints; % popula??es nos checkpoints results(r).elite_checkpoints elite_checkpoints; % elites nos checkpoints results(r).k_checkpoints k_checkpoints; % gera??es dos checkpoints results(r).entropia_x entropia_x; results(r).k_stop k_stop; results(r).motivo_paragem motivo_paragem; results(r).best_merit_final best_merit_final; results(r).pior_el_merit_final pior_el_merit_final; results(r).best_obj_final best_obj_final; % f(melhor) results(r).viavel_final viavel_final; results(r).num_violadas num_violadas; % n? de restri??es violadas results(r).max_violacao max_violacao; % valor máximo de viola??o results(r).melhor_indiv melhor_indiv; results(r).params params; results(r).GA_settings struct(N_pop,N_pop,N_elite,N_elite,P_mut,P_mut,P_cross,P_cross, ... n_bits,n_bits,upper,upper,lower,lower,n_total_bits,n_total_bits); % ---- Print explícito (inclui f(melhor)) ---- fprintf(Run %02d | f(melhor)%.6f | mérito%.6f | viável%d | violadas%d | max_viol%.6f | k_stop%d | %s\n, ... r, best_obj_final, best_merit_final, viavel_final, num_violadas, max_violacao, k_stop, motivo_paragem);end% Selecionar melhor execu??o pelo mérito (ou troca para best_obj_final se quiseres)[~, best_idx] max([results.best_merit_final]);best_run results(best_idx);fprintf(‘\n Melhor run: %d | f(melhor)%.6f | mérito%.6f | viável%d | violadas%d | max_viol%.6f | k_stop%d | %s\n\n’, …best_run.run_id, best_run.best_obj_final, best_run.best_merit_final, best_run.viavel_final, …best_run.num_violadas, best_run.max_violacao, best_run.k_stop, best_run.motivo_paragem);%% ---------------- Plot ONLY da melhor execu??o ----------------best_hist best_run.best_hist;pior_el_hist best_run.pior_el_hist;pen_hist best_run.pen_hist;lambda_h best_run.lambda_hist;vars_hist best_run.best_real_hist; % variáveis (x1,x2,x3,x4) do melhorf_hist best_run.best_f_hist; % f(x) do melhorstd_hist best_run.std_hist; % desvio padr?o das variáveisk_stop best_run.k_stop;motivo best_run.motivo_paragem;melhor best_run.melhor_indiv;entropia_x best_run.entropia_x; % (T x 4) Shannon por variávelpop_checks best_run.pop_checkpoints; % popula??es nos checkpointselite_checks best_run.elite_checkpoints; % elites nos checkpointsk_checks best_run.k_checkpoints; % gera??es dos checkpoints% Valor da fun??o objetivo do melhor indivíduo (já guardado)best_obj_val best_run.best_obj_final;% — Garantir vetores coluna —best_hist best_hist(;pior_el_hist pior_el_hist(;pen_hist pen_hist(;lambda_h lambda_h(;f_hist f_hist(;% vars_hist deve ser matriz (T x 4), n?o precisa de (if ~isempty(vars_hist)vars_hist vars_hist; % já é matrizend% std_hist também é matriz (T x 4)if ~isempty(std_hist)std_hist std_hist; % já é matrizend% std_hist também é matriz (T x 4)if ~isempty(std_hist)std_hist std_hist; % já é matrizend% — Eixo de referência: uma amostra por gera??o (usa best_hist) —T numel(best_hist);t (1:T).;% — Ajustar pior_el_hist e pen_hist ao comprimento T (pad com NaN se faltar) —padNaN (v, TT) [v(; nan(max(0, TT - numel(v)), 1)];if numel(pior_el_hist) T, pior_el_hist pior_el_hist(1:T); else, pior_el_hist padNaN(pior_el_hist, T); endif numel(pen_hist) T, pen_hist pen_hist(1:T); else, pen_hist padNaN(pen_hist, T); endif numel(f_hist) T, f_hist f_hist(1:T); else, f_hist padNaN(f_hist, T); end% Ajustar vars_hist (matriz T x 4)if ~isempty(vars_hist)if size(vars_hist, 1) T% Pad com NaN se faltar linhasvars_hist [vars_hist; nan(T - size(vars_hist, 1), size(vars_hist, 2))];elseif size(vars_hist, 1) T% Truncar se sobrarvars_hist vars_hist(1:T, ;endend% Ajustar std_hist (matriz T x 4)if ~isempty(std_hist)if size(std_hist, 1) Tstd_hist [std_hist; nan(T - size(std_hist, 1), size(std_hist, 2))];elseif size(std_hist, 1) Tstd_hist std_hist(1:T, ;endend% Ajustar std_hist (matriz T x 4)if ~isempty(std_hist)if size(std_hist, 1) Tstd_hist [std_hist; nan(T - size(std_hist, 1), size(std_hist, 2))];elseif size(std_hist, 1) Tstd_hist std_hist(1:T, ;endend% — Ajustar lambda_h: step-hold (repete último valor até T) —if isempty(lambda_h)lambda_h nan(T,1);elseif numel(lambda_h) Tlambda_h [lambda_h; repmat(lambda_h(end), T - numel(lambda_h), 1)];elseif numel(lambda_h) Tlambda_h lambda_h(1:T);end% — Proteger k_stop para n?o ultrapassar T —k_stop_plot min(max(1, k_stop), T);% ---------------- Figura 1: Histórico do Mérito ----------------fig1 figure(‘Name’,‘GA - Histórico do Mérito’,‘Color’,[1 1 1]);plot(t, best_hist, ‘LineWidth’, 2.2, ‘Color’, [0.10 0.45 0.95]); hold on;% A segunda curva continua chamada “mean” no texto/legenda (mesmo sendo min da elite):plot(t, pior_el_hist, ‘–’, ‘LineWidth’, 1.8, ‘Color’, [0.85 0.33 0.10]);% Marcar gera??o de paragemxline(k_stop_plot, ‘:k’, ‘LineWidth’, 1.5, ‘Label’,‘k_{stop}’, ‘LabelOrientation’,‘horizontal’);% ----- Eixo Y baseado APENAS em best_hist -----best_hist_clean best_hist(~isnan(best_hist));ymin min(best_hist_clean);ymax max(best_hist_clean);yrng ymax - ymin;% Padding robusto (mesmo se quase constante)if yrng epspad max(1e-6, 0.05 * max(1, abs(ymax))); % padding mínimoelsepad 0.05 * yrng; % 5% da amplitudeendylim([ymin - pad, ymax pad]);% Ticks informativos: 5–6 marcas baseadas no melhornticks 6;yticks(linspace(ymin, ymax, nticks));% Labels e títuloxlabel(‘Gera??o’);ylabel(‘Mérito (escala do melhor)’); % eixo y só do melhortitle(sprintf(‘GA - Histórico | k_{stop}%d | %s’, k_stop, motivo), ‘Interpreter’,‘none’);% Legenda: mantém “mean” no texto como pedistelegend({‘Melhor’,‘Mínimo da elite’}, ‘Location’,‘best’);grid on;% Mostrar f(melhor) no texto (subtítulo ou annotation)trysubtitle(sprintf(‘f(melhor) %.6g’, best_obj_val), ‘Interpreter’,‘none’); % MATLAB R2020bcatchannotation(fig1, ‘textbox’, [0.15 0.82 0.3 0.08], …‘String’, sprintf(‘f(melhor) %.6g’, best_obj_val), …‘FitBoxToText’,‘on’, ‘EdgeColor’,‘none’, ‘FontWeight’,‘bold’);end% ---------------- Figura 2: Penaliza??o e Lambda ----------------fig2 figure(‘Name’,‘GA - Penaliza??o e Lambda’,‘Color’,[1 1 1]);yyaxis left;plot(t, pen_hist, ‘LineWidth’,1.8, ‘Color’,[0.15 0.7 0.25]);ylabel(‘Penaliza??o (melhor)’);yyaxis right;plot(t, lambda_h, ‘–’, ‘LineWidth’,1.8, ‘Color’,[0.6 0.2 0.8]);ylabel(‘\lambda’);xlabel(‘Gera??o’);title(‘Penaliza??o do melhor e evolu??o de \lambda’);grid on;% ---------------- Figura 3: Evolu??o de f(x) do Melhor ----------------fig3 figure(‘Name’,‘GA - Evolu??o de f(x)’,‘Color’,[1 1 1]);plot(t, f_hist, ‘LineWidth’, 2.2, ‘Color’, [0.85 0.15 0.15]); hold on;% Marcar gera??o de paragemxline(k_stop_plot, ‘:k’, ‘LineWidth’, 1.5, ‘Label’,‘k_{stop}’, ‘LabelOrientation’,‘horizontal’);% Eixo Y baseado em f_histf_hist_clean f_hist(~isnan(f_hist));if ~isempty(f_hist_clean)ymin_r min(f_hist_clean);ymax_r max(f_hist_clean);yrng_r ymax_r - ymin_r;if yrng_r eps pad_r max(1e-6, 0.05 * max(1, abs(ymax_r))); else pad_r 0.05 * yrng_r; end ylim([ymin_r - pad_r, ymax_r pad_r]); yticks(linspace(ymin_r, ymax_r, 6));endxlabel(‘Gera??o’);ylabel(‘f(x) - Fun??o Objetivo’);title(sprintf(‘Evolu??o de f(x) do Melhor Indivíduo | f_{final}%.6g’, best_obj_val), ‘Interpreter’,‘none’);grid on;% Adicionar linha horizontal com o valor finalyline(best_obj_val, ‘–b’, sprintf(‘f_{final}%.4g’, best_obj_val), …‘LineWidth’, 1.2, ‘LabelHorizontalAlignment’, ‘left’);% ---------------- Figura 4: Evolu??o das Variáveis do Melhor ----------------fig4 figure(‘Name’,‘GA - Evolu??o das Variáveis’,‘Color’,[1 1 1]);% Nomes das variáveis (ajusta se necessário)var_names {‘h (espessura)’, ‘l (comprimento)’, ‘t (largura)’, ‘b (altura)’};var_colors [0.00, 0.45, 0.74; % azul0.85, 0.33, 0.10; % laranja0.93, 0.69, 0.13; % amarelo0.49, 0.18, 0.56 % roxo];% Verificar se vars_hist existe e tem 4 colunasif ~isempty(vars_hist) size(vars_hist, 2) 4for i 1:4 subplot(2, 2, i); plot(t, vars_hist(:, i), LineWidth, 2.0, Color, var_colors(i, :)); hold on; % Marcar k_stop xline(k_stop_plot, :k, LineWidth, 1.2); % Valor final val_final melhor(i); yline(val_final, --, sprintf(%.4g, val_final), ... Color, var_colors(i, :), LineWidth, 1.0, ... LabelHorizontalAlignment, left); % Definir limites do eixo Y baseados nos bounds y_lower lower(i); y_upper upper(i); y_range y_upper - y_lower; y_pad 0.05 * y_range; % 5% de padding ylim([y_lower - y_pad, y_upper y_pad]); % Adicionar linhas horizontais com os limites yline(y_lower, :, Lower, Color, [0.5 0.5 0.5], LineWidth, 0.8, ... LabelHorizontalAlignment, right, Alpha, 0.5); yline(y_upper, :, Upper, Color, [0.5 0.5 0.5], LineWidth, 0.8, ... LabelHorizontalAlignment, right, Alpha, 0.5); xlabel(Gera??o); ylabel(sprintf(%s, var_names{i})); title(sprintf(Variável x_%d: %s, i, var_names{i})); grid on; end2 通用运行步骤1直接运行main.m即可一键出图⛄三、运行结果⛄四、matlab版本及参考文献1 matlab版本2019b2 参考文献[1]王庆荣,朱昌盛,梁剑波,冯文熠.基于遗传算法的公交智能排班系统应用研究[J].计算机仿真. 2011,28(03)3 备注简介此部分摘自互联网仅供参考若侵权联系删除⛄五、仿真咨询与程序定制1 各类智能优化算法改进及应用1.1 PID优化1.2 VMD优化1.3 配电网重构1.4 三维装箱1.5 微电网优化1.6 优化布局1.7 优化参数1.8 优化成本1.9 优化充电1.10 优化调度1.11 优化电价1.12 优化发车1.13 优化分配1.14 优化覆盖1.15 优化控制1.16 优化库存1.17 优化路由1.18 优化设计1.19 优化位置1.20 优化吸波1.21 优化选址1.22 优化运行1.23 优化指派1.24 优化组合1.25 车间调度1.26 生产调度1.27 经济调度1.28 装配线调度1.29 水库调度1.30 货位优化1.31 公交排班优化1.32 集装箱船配载优化1.33 水泵组合优化1.34 医疗资源分配优化1.35 可视域基站和无人机选址优化2 机器学习和深度学习分类与预测2.1 机器学习和深度学习分类2.1.1 BiLSTM双向长短时记忆神经网络分类2.1.2 BP神经网络分类2.1.3 CNN卷积神经网络分类2.1.4 DBN深度置信网络分类2.1.5 DELM深度学习极限学习机分类2.1.6 ELMAN递归神经网络分类2.1.7 ELM极限学习机分类2.1.8 GRNN广义回归神经网络分类2.1.9 GRU门控循环单元分类2.1.10 KELM混合核极限学习机分类2.1.11 KNN分类2.1.12 LSSVM最小二乘法支持向量机分类2.1.13 LSTM长短时记忆网络分类2.1.14 MLP全连接神经网络分类2.1.15 PNN概率神经网络分类2.1.16 RELM鲁棒极限学习机分类2.1.17 RF随机森林分类2.1.18 SCN随机配置网络模型分类2.1.19 SVM支持向量机分类2.1.20 XGBOOST分类2.2 机器学习和深度学习预测2.2.1 ANFIS自适应模糊神经网络预测2.2.2 ANN人工神经网络预测2.2.3 ARMA自回归滑动平均模型预测2.2.4 BF粒子滤波预测2.2.5 BiLSTM双向长短时记忆神经网络预测2.2.6 BLS宽度学习神经网络预测2.2.7 BP神经网络预测2.2.8 CNN卷积神经网络预测2.2.9 DBN深度置信网络预测2.2.10 DELM深度学习极限学习机预测2.2.11 DKELM回归预测2.2.12 ELMAN递归神经网络预测2.2.13 ELM极限学习机预测2.2.14 ESN回声状态网络预测2.2.15 FNN前馈神经网络预测2.2.16 GMDN预测2.2.17 GMM高斯混合模型预测2.2.18 GRNN广义回归神经网络预测2.2.19 GRU门控循环单元预测2.2.20 KELM混合核极限学习机预测2.2.21 LMS最小均方算法预测2.2.22 LSSVM最小二乘法支持向量机预测2.2.23 LSTM长短时记忆网络预测2.2.24 RBF径向基函数神经网络预测2.2.25 RELM鲁棒极限学习机预测2.2.26 RF随机森林预测2.2.27 RNN循环神经网络预测2.2.28 RVM相关向量机预测2.2.29 SVM支持向量机预测2.2.30 TCN时间卷积神经网络预测2.2.31 XGBoost回归预测2.2.32 模糊预测2.2.33 奇异谱分析方法SSA时间序列预测2.3 机器学习和深度学习实际应用预测CPI指数预测、PM2.5浓度预测、SOC预测、财务预警预测、产量预测、车位预测、虫情预测、带钢厚度预测、电池健康状态预测、电力负荷预测、房价预测、腐蚀率预测、故障诊断预测、光伏功率预测、轨迹预测、航空发动机寿命预测、汇率预测、混凝土强度预测、加热炉炉温预测、价格预测、交通流预测、居民消费指数预测、空气质量预测、粮食温度预测、气温预测、清水值预测、失业率预测、用电量预测、运输量预测、制造业采购经理指数预测3 图像处理方面3.1 图像边缘检测3.2 图像处理3.3 图像分割3.4 图像分类3.5 图像跟踪3.6 图像加密解密3.7 图像检索3.8 图像配准3.9 图像拼接3.10 图像评价3.11 图像去噪3.12 图像融合3.13 图像识别3.13.1 表盘识别3.13.2 车道线识别3.13.3 车辆计数3.13.4 车辆识别3.13.5 车牌识别3.13.6 车位识别3.13.7 尺寸检测3.13.8 答题卡识别3.13.9 电器识别3.13.10 跌倒检测3.13.11 动物识别3.13.12 二维码识别3.13.13 发票识别3.13.14 服装识别3.13.15 汉字识别3.13.16 红绿灯识别3.13.17 虹膜识别3.13.18 火灾检测3.13.19 疾病分类3.13.20 交通标志识别3.13.21 卡号识别3.13.22 口罩识别3.13.23 裂缝识别3.13.24 目标跟踪3.13.25 疲劳检测3.13.26 旗帜识别3.13.27 青草识别3.13.28 人脸识别3.13.29 人民币识别3.13.30 身份证识别3.13.31 手势识别3.13.32 数字字母识别3.13.33 手掌识别3.13.34 树叶识别3.13.35 水果识别3.13.36 条形码识别3.13.37 温度检测3.13.38 瑕疵检测3.13.39 芯片检测3.13.40 行为识别3.13.41 验证码识别3.13.42 药材识别3.13.43 硬币识别3.13.44 邮政编码识别3.13.45 纸牌识别3.13.46 指纹识别3.14 图像修复3.15 图像压缩3.16 图像隐写3.17 图像增强3.18 图像重建4 路径规划方面4.1 旅行商问题TSP4.1.1 单旅行商问题TSP4.1.2 多旅行商问题MTSP4.2 车辆路径问题VRP4.2.1 车辆路径问题VRP4.2.2 带容量的车辆路径问题CVRP4.2.3 带容量时间窗距离车辆路径问题DCTWVRP4.2.4 带容量距离车辆路径问题DCVRP4.2.5 带距离的车辆路径问题DVRP4.2.6 带充电站时间窗车辆路径问题ETWVRP4.2.7 带多种容量的车辆路径问题MCVRP4.2.8 带距离的多车辆路径问题MDVRP4.2.9 同时取送货的车辆路径问题SDVRP4.2.10 带时间窗容量的车辆路径问题TWCVRP4.2.11 带时间窗的车辆路径问题TWVRP4.3 多式联运运输问题4.4 机器人路径规划4.4.1 避障路径规划4.4.2 机器人轨迹跟踪4.4.3 机器人编队4.4.4 机器人导航4.4.5 机器人定位4.4.6 机器人控制4.4.7 机器人路径规划4.4.8 机器人任务分配4.4.9 机器人搜索4.4.10 机器人运输4.4.11 迷宫路径规划4.4.12 水下机器人路径规划4.4.13 栅格地图路径规划4.5 配送路径规划4.5.1 冷链配送路径规划4.5.2 外卖配送路径规划4.5.3 口罩配送路径规划4.5.4 药品配送路径规划4.5.5 含充电站配送路径规划4.5.6 连锁超市配送路径规划4.5.7 车辆协同无人机配送路径规划4.6 无人机路径规划4.6.1 飞行器仿真4.6.2 无人机飞行作业4.6.3 无人机轨迹跟踪4.6.4 无人机集群仿真4.6.5 卡车无人机4.6.6 目标搜索4.6.7 三维路径规划4.6.8 无人机编队4.6.9 无人机导航4.6.10 无人机吊运4.6.11 无人机对抗4.6.12 无人机覆盖4.6.13 无人机检测4.6.14 无人机控制4.6.15 无人机求援4.6.16 无人机位姿估计4.6.17 无人机系统4.6.18 无人机侦查4.6.19 无人机联盟4.6.20 无人机协同任务4.6.21 异构无人机4.7 水下飞行器4.8 无人车路径规划4.9 无人艇路径规划4.10 物流选址4.11 车辆控制4.12 多智能体路径规划4.12.1 多智能体编队4.12.2 多智能体控制4.12.3 多智能体路径规划4.12.4 多智能体协同5 语音处理5.1 语音情感识别5.2 声源定位5.3 特征提取5.4 语音编码5.5 语音处理5.6 语音分离5.7 语音分析5.8 语音合成5.9 语音加密5.10 语音去噪5.11 语音识别5.12 语音压缩5.13 语音隐藏6 元胞自动机方面6.1 元胞自动机病毒仿真6.2 元胞自动机城市规划6.3 元胞自动机交通流6.4 元胞自动机气体6.5 元胞自动机人员疏散6.6 元胞自动机森林火灾6.7 元胞自动机生命游戏7 信号处理7.1 机械振动信号处理7.1.1 故障信号7.1.1.1 齿轮损伤识别7.1.1.2 异步电机转子断条故障诊断7.1.1.3 滚动体内外圈故障诊断分析7.1.1.4 电机故障诊断分析7.1.1.5 轴承故障诊断分析7.1.1.6 齿轮箱故障诊断分析7.1.1.7 三相逆变器故障诊断分析7.1.1.8 柴油机故障诊断7.1.2 管道泄漏7.1.3 振动信号7.2 生物医学信号处理7.2.1 肌电信号EMG7.2.2 脑电信号EEG7.2.3 乳腺癌诊断7.2.4 生物电信号7.2.5 心电信号ECG7.2.6 心血管7.2.7 心脏仿真7.2.8 血压7.3 声呐探测信号处理7.3.1 声呐7.3.2 水声通信7.4 通信工程信号处理7.4.1 超声波信号处理7.4.2 导航定位7.4.3 航天航空7.4.4 空间识别7.4.5 雷达通信7.4.5.1 FMCW仿真7.4.5.2 GPS抗干扰7.4.5.3 雷达LFM7.4.5.4 雷达MIMO7.4.5.5 雷达测角7.4.5.6 雷达成像7.4.5.7 雷达定位7.4.5.8 雷达回波7.4.5.9 雷达检测7.4.5.10 雷达数字信号处理7.4.5.11 雷达通信7.4.5.12 雷达相控阵7.4.5.13 雷达信号分析7.4.5.14 雷达预警7.4.5.15 雷达脉冲压缩7.4.5.16 天线方向图7.4.5.17 雷达杂波仿真7.4.6 模拟信号处理7.4.7 时差绘制7.4.8 数字信道处理7.4.9 数字信号处理7.4.10 无线通信7.4.11 姿态解算**7.4.12 资源分配 **7.5 自动测量信号处理**7.5.1 参数估计 ****7.5.2 电动汽车动力电池管理 ****7.5.3 高度预估 **8 物理应用8.1 物理学分支力学8.1.1 材料力学8.1.2 弹性力学8.1.3 动力学8.1.4 分析力学8.1.5 固体力学8.1.6 结构力学8.1.7 静力学8.1.8 空气动力学8.1.9 流体力学8.1.10 塑性力学8.1.11 运动学8.2 物理学其他分支8.2.1 表面物理学8.2.2 超声学8.2.3 次声学8.2.4 电磁学8.2.5 电动力学8.2.6 电学8.2.7 高压物理学8.2.8 金属物理学8.2.9 光学8.2.10 核物理学8.2.11 金属物理学8.2.12 量子力学8.2.13 热力学8.2.14 热学8.2.15 声学8.2.16 水声学8.3 物理应用8.3.1 地理学8.3.2 化学化工8.3.3 生物8.3.4 其他