{"id":60076,"date":"2026-01-14T22:26:36","date_gmt":"2026-01-14T14:26:36","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/60076.html"},"modified":"2026-01-14T22:26:36","modified_gmt":"2026-01-14T14:26:36","slug":"%e3%80%90%e6%97%a0%e4%ba%ba%e6%9c%ba%e7%bc%96%e9%98%9f%e3%80%91%e7%b2%92%e5%ad%90%e7%be%a4%e7%ae%97%e6%b3%95pso%e5%a4%9a%e6%97%a0%e4%ba%ba%e6%9c%bauav%e7%be%a4%e4%bd%93%e5%8d%8f%e5%90%8c%e8%bd%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/60076.html","title":{"rendered":"\u3010\u65e0\u4eba\u673a\u7f16\u961f\u3011\u7c92\u5b50\u7fa4\u7b97\u6cd5PSO\u591a\u65e0\u4eba\u673aUAV\u7fa4\u4f53\u534f\u540c\u8f68\u8ff9\u89c4\u5212\u3010\u542bMatlab\u6e90\u7801 14919\u671f\u3011"},"content":{"rendered":"

💥💥💥💥💥💥💥💥💞💞💞💞💞💞💞💞💞Matlab\u9886\u57df\u535a\u5ba2\u4e4b\u5bb6💞💞💞💞💞💞💞💞💞💥💥💥💥💥💥💥💥 🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀 \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" 🔊\u535a\u4e3b\u7b80\u4ecb：985\u7814\u7a76\u751f，Matlab\u9886\u57df\u79d1\u7814\u5f00\u53d1\u8005； 🏫\u4e2a\u4eba\u4e3b\u9875：Matlab\u9886\u57df 🏆\u4ee3\u7801\u83b7\u53d6\u65b9\u5f0f： CSDN Matlab\u9886\u57df\u2014\u4ee3\u7801\u83b7\u53d6\u65b9\u5f0f<\/p>\n

🚅\u5ea7\u53f3\u94ed：\u8def\u6f2b\u6f2b\u5176\u4fee\u8fdc\u516e，\u543e\u5c06\u4e0a\u4e0b\u800c\u6c42\u7d22\u3002 \u66f4\u591aMatlab\u8def\u5f84\u89c4\u5212\u4eff\u771f\u5185\u5bb9\u70b9\u51fb👇 \u2460Matlab\u8def\u5f84\u89c4\u5212（\u9ad8\u9636\u7248） \u2461\u4ed8\u8d39\u4e13\u680fMatlab\u8def\u5f84\u89c4\u5212（\u8fdb\u9636\u7248） \u2462\u4ed8\u8d39\u4e13\u680fMatlab\u8def\u5f84\u89c4\u5212（\u521d\u7ea7\u7248）<\/p>\n

\u26f3\ufe0f\u5173\u6ce8CSDN Matlab\u9886\u57df，\u66f4\u591a\u8d44\u6e90\u7b49\u4f60\u6765！！<\/p>\n

\u26c4\u4e00\u3001\u7c92\u5b50\u7fa4\u7b97\u6cd5PSO\u591a\u65e0\u4eba\u673aUAV)\u7fa4\u4f53\u534f\u540c\u8f68\u8ff9\u89c4\u5212<\/h3>\n

\u7c92\u5b50\u7fa4\u7b97\u6cd5（PSO）\u662f\u4e00\u79cd\u57fa\u4e8e\u7fa4\u4f53\u667a\u80fd\u7684\u4f18\u5316\u7b97\u6cd5，\u9002\u7528\u4e8e\u89e3\u51b3\u591a\u65e0\u4eba\u673a\u534f\u540c\u8f68\u8ff9\u89c4\u5212\u95ee\u9898\u3002\u901a\u8fc7\u6a21\u62df\u9e1f\u7fa4\u6216\u9c7c\u7fa4\u7684\u793e\u4f1a\u884c\u4e3a，PSO\u80fd\u591f\u9ad8\u6548\u5730\u641c\u7d22\u6700\u4f18\u89e3\u7a7a\u95f4\u3002<\/p>\n

1 PSO\u5728\u591a\u65e0\u4eba\u673a\u8f68\u8ff9\u89c4\u5212\u4e2d\u7684\u6838\u5fc3\u6b65\u9aa4<\/p>\n

\u521d\u59cb\u5316\u7c92\u5b50\u7fa4 \u5b9a\u4e49\u7c92\u5b50\u7fa4\u89c4\u6a21，\u6bcf\u4e2a\u7c92\u5b50\u4ee3\u8868\u4e00\u79cd\u53ef\u80fd\u7684\u65e0\u4eba\u673a\u8f68\u8ff9\u65b9\u6848\u3002\u7c92\u5b50\u4f4d\u7f6e\u548c\u901f\u5ea6\u968f\u673a\u521d\u59cb\u5316，\u9700\u6ee1\u8db3\u65e0\u4eba\u673a\u52a8\u529b\u5b66\u7ea6\u675f\u548c\u73af\u5883\u7ea6\u675f\u3002<\/p>\n

\u9002\u5e94\u5ea6\u51fd\u6570\u8bbe\u8ba1 \u9002\u5e94\u5ea6\u51fd\u6570\u9700\u7efc\u5408\u8003\u8651\u8f68\u8ff9\u957f\u5ea6\u3001\u907f\u969c\u3001\u80fd\u8017\u548c\u534f\u540c\u6027\u3002\u4f8b\u5982： [ f = w_1 \\\\cdot \\\\text{\u8def\u5f84\u957f\u5ea6} + w_2 \\\\cdot \\\\text{\u78b0\u649e\u4ee3\u4ef7} + w_3 \\\\cdot \\\\text{\u65f6\u95f4\u540c\u6b65\u8bef\u5dee} ] \u6743\u91cd\u7cfb\u6570 (w_1, w_2, w_3) \u6839\u636e\u4efb\u52a1\u9700\u6c42\u8c03\u6574\u3002<\/p>\n

\u66f4\u65b0\u7c92\u5b50\u72b6\u6001 \u6839\u636e\u4e2a\u4f53\u6700\u4f18（pbest）\u548c\u5168\u5c40\u6700\u4f18（gbest）\u66f4\u65b0\u7c92\u5b50\u901f\u5ea6\u548c\u4f4d\u7f6e： [ v_{i}^{k+1} = \\\\omega v_{i}^{k} + c_1 r_1 (pbest_i – x_i^k) + c_2 r_2 (gbest – x_i^k) ] [ x_{i}^{k+1} = x_i^k + v_i^{k+1} ] \u60ef\u6027\u6743\u91cd (\\\\omega) \u548c\u5b66\u4e60\u56e0\u5b50 (c_1, c_2) \u9700\u52a8\u6001\u8c03\u6574\u4ee5\u63d0\u9ad8\u6536\u655b\u6027\u3002<\/p>\n

2 \u591a\u65e0\u4eba\u673a\u534f\u540c\u7ea6\u675f\u5904\u7406<\/p>\n

\u907f\u78b0\u7ea6\u675f \u901a\u8fc7\u6392\u65a5\u529b\u6a21\u578b\u6216\u5b89\u5168\u8ddd\u79bb\u7ea6\u675f\u786e\u4fdd\u65e0\u4eba\u673a\u95f4\u4e0d\u53d1\u751f\u78b0\u649e\u3002\u9002\u5e94\u5ea6\u51fd\u6570\u4e2d\u5f15\u5165\u60e9\u7f5a\u9879： [ \\\\text{\u78b0\u649e\u4ee3\u4ef7} = \\\\sum_{i \\\\neq j} \\\\max(0, d_{safe} – |p_i – p_j|) ]<\/p>\n

\u901a\u4fe1\u7ea6\u675f \u4fdd\u6301\u65e0\u4eba\u673a\u95f4\u901a\u4fe1\u8fde\u901a\u6027，\u53ef\u901a\u8fc7\u6700\u5c0f\u751f\u6210\u6811\u6216\u52bf\u573a\u6cd5\u5b9e\u73b0\u3002\u9002\u5e94\u5ea6\u51fd\u6570\u589e\u52a0\u8fde\u901a\u6027\u60e9\u7f5a\u9879\u3002<\/p>\n

\u65f6\u95f4\u534f\u540c \u786e\u4fdd\u6240\u6709\u65e0\u4eba\u673a\u540c\u65f6\u5230\u8fbe\u76ee\u6807\u70b9\u3002\u5f15\u5165\u65f6\u95f4\u540c\u6b65\u8bef\u5dee\u9879： [ \\\\text{\u540c\u6b65\u8bef\u5dee} = \\\\max(t_i) – \\\\min(t_i) ]<\/p>\n

3 \u7b97\u6cd5\u4f18\u5316\u7b56\u7565<\/p>\n

\u52a8\u6001\u53c2\u6570\u8c03\u6574 \u91c7\u7528\u7ebf\u6027\u9012\u51cf\u7b56\u7565\u8c03\u6574\u60ef\u6027\u6743\u91cd： [ \\\\omega = \\\\omega_{max} – (\\\\omega_{max} – \\\\omega_{min}) \\\\cdot \\\\frac{k}{K} ] \u8fed\u4ee3\u6b21\u6570 (k) \u548c\u603b\u8fed\u4ee3\u6b21\u6570 (K)\u3002<\/p>\n

\u6df7\u5408\u4f18\u5316\u65b9\u6cd5 \u7ed3\u5408\u9057\u4f20\u7b97\u6cd5（GA）\u7684\u53d8\u5f02\u64cd\u4f5c\u6216\u6a21\u62df\u9000\u706b（SA）\u7684\u63a5\u53d7\u51c6\u5219，\u907f\u514d\u65e9\u719f\u6536\u655b\u3002<\/p>\n

\u5e76\u884c\u5316\u5904\u7406 \u5229\u7528\u591a\u6838CPU\u6216GPU\u52a0\u901f\u7c92\u5b50\u7fa4\u8bc4\u4f30，\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u65e0\u4eba\u673a\u96c6\u7fa4\u3002<\/p>\n

4 \u5b9e\u73b0\u793a\u4f8b（Python\u4f2a\u4ee3\u7801）<\/p>\n

class<\/span> Particle<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> trajectory)<\/span>:<\/span>
\n self.<\/span>position =<\/span> trajectory
\n self.<\/span>velocity =<\/span> random_velocity(<\/span>)<\/span>
\n self.<\/span>best_position =<\/span> None<\/span>
\n self.<\/span>best_fitness =<\/span> float<\/span>(<\/span>'inf'<\/span>)<\/span><\/p>\n

def<\/span> fitness<\/span>(<\/span>trajectory)<\/span>:<\/span>
\n length =<\/span> calculate_path_length(<\/span>trajectory)<\/span>
\n collision =<\/span> check_collision(<\/span>trajectory)<\/span>
\n sync_error =<\/span> calculate_sync_error(<\/span>trajectory)<\/span>
\n return<\/span> w1*<\/span>length +<\/span> w2*<\/span>collision +<\/span> w3*<\/span>sync_error<\/p>\n

def<\/span> pso_optimize<\/span>(<\/span>)<\/span>:<\/span>
\n swarm =<\/span> initialize_swarm(<\/span>)<\/span>
\n gbest =<\/span> min<\/span>(<\/span>particle.<\/span>best_fitness for<\/span> particle in<\/span> swarm)<\/span><\/p>\n

for<\/span> iteration in<\/span> range<\/span>(<\/span>max_iter)<\/span>:<\/span>
\n for<\/span> particle in<\/span> swarm:<\/span>
\n particle.<\/span>velocity =<\/span> update_velocity(<\/span>particle,<\/span> gbest)<\/span>
\n particle.<\/span>position =<\/span> update_position(<\/span>particle)<\/span>
\n current_fitness =<\/span> fitness(<\/span>particle.<\/span>position)<\/span>
\n if<\/span> current_fitness <<\/span> particle.<\/span>best_fitness:<\/span>
\n particle.<\/span>best_position =<\/span> particle.<\/span>position
\n particle.<\/span>best_fitness =<\/span> current_fitness
\n gbest =<\/span> update_global_best(<\/span>swarm)<\/span>
\n return<\/span> gbest<\/p>\n

5 \u5b9e\u9645\u5e94\u7528\u6ce8\u610f\u4e8b\u9879<\/p>\n

\u73af\u5883\u5efa\u6a21 \u4f7f\u7528\u6805\u683c\u5730\u56fe\u6216\u4e09\u7ef4\u70b9\u4e91\u51c6\u786e\u8868\u793a\u969c\u788d\u7269，\u786e\u4fdd\u8f68\u8ff9\u53ef\u884c\u6027\u3002<\/p>\n

\u5b9e\u65f6\u6027\u8981\u6c42 \u9488\u5bf9\u52a8\u6001\u73af\u5883，\u91c7\u7528\u6eda\u52a8\u65f6\u57df\u4f18\u5316（RHC）\u6846\u67b6，\u5c06PSO\u5d4c\u5165\u5c40\u90e8\u91cd\u89c4\u5212\u5faa\u73af\u3002<\/p>\n

\u786c\u4ef6\u9650\u5236 \u8003\u8651\u65e0\u4eba\u673a\u673a\u52a8\u6027\u548c\u4f20\u611f\u5668\u8303\u56f4，\u5728\u7b97\u6cd5\u4e2d\u5d4c\u5165\u52a8\u529b\u5b66\u6a21\u578b\u7ea6\u675f： [ |\\\\dot{p}i| \\\\leq v{max}, \\\\quad |\\\\ddot{p}i| \\\\leq a{max} ]<\/p>\n

\u26c4\u4e8c\u3001\u90e8\u5206\u6e90\u4ee3\u7801\u548c\u8fd0\u884c\u6b65\u9aa4<\/h3>\n

1 \u90e8\u5206\u4ee3\u7801<\/p>\n

2 \u901a\u7528\u8fd0\u884c\u6b65\u9aa4 （1）\u76f4\u63a5\u8fd0\u884cmain.m\u5373\u53ef\u4e00\u952e\u51fa\u56fe<\/p>\n

\u26c4\u4e09\u3001\u8fd0\u884c\u7ed3\u679c<\/h3>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u26c4\u56db\u3001matlab\u7248\u672c\u53ca\u53c2\u8003\u6587\u732e<\/h3>\n

1 matlab\u7248\u672c 2019b<\/p>\n

2 \u53c2\u8003\u6587\u732e [1]\u4e07\u5b87.\u57fa\u4e8e\u7b56\u7565\u534f\u540c\u7684\u65e0\u4eba\u673a\u7f16\u961f\u63a7\u5236\u4e0e\u9632\u649e\u65b9\u6cd5\u7814\u7a76[D].\u56fd\u9632\u79d1\u6280\u5927\u5b66. 2019<\/p>\n

3 \u5907\u6ce8 \u7b80\u4ecb\u6b64\u90e8\u5206\u6458\u81ea\u4e92\u8054\u7f51，\u4ec5\u4f9b\u53c2\u8003，\u82e5\u4fb5\u6743，\u8054\u7cfb\u5220\u9664<\/p>\n

🍅 \u4eff\u771f\u54a8\u8be2 1 \u5404\u7c7b\u667a\u80fd\u4f18\u5316\u7b97\u6cd5\u6539\u8fdb\u53ca\u5e94\u7528 1.1 PID\u4f18\u5316 1.2 VMD\u4f18\u5316 1.3 \u914d\u7535\u7f51\u91cd\u6784 1.4 \u4e09\u7ef4\u88c5\u7bb1 1.5 \u5fae\u7535\u7f51\u4f18\u5316 1.6 \u4f18\u5316\u5e03\u5c40 1.7 \u4f18\u5316\u53c2\u6570 1.8 \u4f18\u5316\u6210\u672c 1.9 \u4f18\u5316\u5145\u7535 1.10 \u4f18\u5316\u8c03\u5ea6 1.11 \u4f18\u5316\u7535\u4ef7 1.12 \u4f18\u5316\u53d1\u8f66 1.13 \u4f18\u5316\u5206\u914d 1.14 \u4f18\u5316\u8986\u76d6 1.15 \u4f18\u5316\u63a7\u5236 1.16 \u4f18\u5316\u5e93\u5b58 1.17 \u4f18\u5316\u8def\u7531 1.18 \u4f18\u5316\u8bbe\u8ba1 1.19 \u4f18\u5316\u4f4d\u7f6e 1.20 \u4f18\u5316\u5438\u6ce2 1.21 \u4f18\u5316\u9009\u5740 1.22 \u4f18\u5316\u8fd0\u884c 1.23 \u4f18\u5316\u6307\u6d3e 1.24 \u4f18\u5316\u7ec4\u5408 1.25 \u8f66\u95f4\u8c03\u5ea6 1.26 \u751f\u4ea7\u8c03\u5ea6 1.27 \u7ecf\u6d4e\u8c03\u5ea6 1.28 \u88c5\u914d\u7ebf\u8c03\u5ea6 1.29 \u6c34\u5e93\u8c03\u5ea6 1.30 \u8d27\u4f4d\u4f18\u5316 1.31 \u516c\u4ea4\u6392\u73ed\u4f18\u5316 1.32 \u96c6\u88c5\u7bb1\u8239\u914d\u8f7d\u4f18\u5316 1.33 \u6c34\u6cf5\u7ec4\u5408\u4f18\u5316 1.34 \u533b\u7597\u8d44\u6e90\u5206\u914d\u4f18\u5316 1.35 \u53ef\u89c6\u57df\u57fa\u7ad9\u548c\u65e0\u4eba\u673a\u9009\u5740\u4f18\u5316<\/p>\n

2 \u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u5206\u7c7b\u4e0e\u9884\u6d4b 2.1 \u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u5206\u7c7b 2.1.1 BiLSTM\u53cc\u5411\u957f\u77ed\u65f6\u8bb0\u5fc6\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.2 BP\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.3 CNN\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.4 DBN\u6df1\u5ea6\u7f6e\u4fe1\u7f51\u7edc\u5206\u7c7b 2.1.5 DELM\u6df1\u5ea6\u5b66\u4e60\u6781\u9650\u5b66\u4e60\u673a\u5206\u7c7b 2.1.6 ELMAN\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.7 ELM\u6781\u9650\u5b66\u4e60\u673a\u5206\u7c7b 2.1.8 GRNN\u5e7f\u4e49\u56de\u5f52\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.9 GRU\u95e8\u63a7\u5faa\u73af\u5355\u5143\u5206\u7c7b 2.1.10 KELM\u6df7\u5408\u6838\u6781\u9650\u5b66\u4e60\u673a\u5206\u7c7b 2.1.11 KNN\u5206\u7c7b 2.1.12 LSSVM\u6700\u5c0f\u4e8c\u4e58\u6cd5\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b 2.1.13 LSTM\u957f\u77ed\u65f6\u8bb0\u5fc6\u7f51\u7edc\u5206\u7c7b 2.1.14 MLP\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.15 PNN\u6982\u7387\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b 2.1.16 RELM\u9c81\u68d2\u6781\u9650\u5b66\u4e60\u673a\u5206\u7c7b 2.1.17 RF\u968f\u673a\u68ee\u6797\u5206\u7c7b 2.1.18 SCN\u968f\u673a\u914d\u7f6e\u7f51\u7edc\u6a21\u578b\u5206\u7c7b 2.1.19 SVM\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b 2.1.20 XGBOOST\u5206\u7c7b<\/p>\n

2.2 \u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u9884\u6d4b 2.2.1 ANFIS\u81ea\u9002\u5e94\u6a21\u7cca\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.2 ANN\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.3 ARMA\u81ea\u56de\u5f52\u6ed1\u52a8\u5e73\u5747\u6a21\u578b\u9884\u6d4b 2.2.4 BF\u7c92\u5b50\u6ee4\u6ce2\u9884\u6d4b 2.2.5 BiLSTM\u53cc\u5411\u957f\u77ed\u65f6\u8bb0\u5fc6\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.6 BLS\u5bbd\u5ea6\u5b66\u4e60\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.7 BP\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.8 CNN\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.9 DBN\u6df1\u5ea6\u7f6e\u4fe1\u7f51\u7edc\u9884\u6d4b 2.2.10 DELM\u6df1\u5ea6\u5b66\u4e60\u6781\u9650\u5b66\u4e60\u673a\u9884\u6d4b 2.2.11 DKELM\u56de\u5f52\u9884\u6d4b 2.2.12 ELMAN\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.13 ELM\u6781\u9650\u5b66\u4e60\u673a\u9884\u6d4b 2.2.14 ESN\u56de\u58f0\u72b6\u6001\u7f51\u7edc\u9884\u6d4b 2.2.15 FNN\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.16 GMDN\u9884\u6d4b 2.2.17 GMM\u9ad8\u65af\u6df7\u5408\u6a21\u578b\u9884\u6d4b 2.2.18 GRNN\u5e7f\u4e49\u56de\u5f52\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.19 GRU\u95e8\u63a7\u5faa\u73af\u5355\u5143\u9884\u6d4b 2.2.20 KELM\u6df7\u5408\u6838\u6781\u9650\u5b66\u4e60\u673a\u9884\u6d4b 2.2.21 LMS\u6700\u5c0f\u5747\u65b9\u7b97\u6cd5\u9884\u6d4b 2.2.22 LSSVM\u6700\u5c0f\u4e8c\u4e58\u6cd5\u652f\u6301\u5411\u91cf\u673a\u9884\u6d4b 2.2.23 LSTM\u957f\u77ed\u65f6\u8bb0\u5fc6\u7f51\u7edc\u9884\u6d4b 2.2.24 RBF\u5f84\u5411\u57fa\u51fd\u6570\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.25 RELM\u9c81\u68d2\u6781\u9650\u5b66\u4e60\u673a\u9884\u6d4b 2.2.26 RF\u968f\u673a\u68ee\u6797\u9884\u6d4b 2.2.27 RNN\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.28 RVM\u76f8\u5173\u5411\u91cf\u673a\u9884\u6d4b 2.2.29 SVM\u652f\u6301\u5411\u91cf\u673a\u9884\u6d4b 2.2.30 TCN\u65f6\u95f4\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b 2.2.31 XGBoost\u56de\u5f52\u9884\u6d4b 2.2.32 \u6a21\u7cca\u9884\u6d4b 2.2.33 \u5947\u5f02\u8c31\u5206\u6790\u65b9\u6cd5SSA\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/p>\n

2.3 \u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u5b9e\u9645\u5e94\u7528\u9884\u6d4b CPI\u6307\u6570\u9884\u6d4b\u3001PM2.5\u6d53\u5ea6\u9884\u6d4b\u3001SOC\u9884\u6d4b\u3001\u8d22\u52a1\u9884\u8b66\u9884\u6d4b\u3001\u4ea7\u91cf\u9884\u6d4b\u3001\u8f66\u4f4d\u9884\u6d4b\u3001\u866b\u60c5\u9884\u6d4b\u3001\u5e26\u94a2\u539a\u5ea6\u9884\u6d4b\u3001\u7535\u6c60\u5065\u5eb7\u72b6\u6001\u9884\u6d4b\u3001\u7535\u529b\u8d1f\u8377\u9884\u6d4b\u3001\u623f\u4ef7\u9884\u6d4b\u3001\u8150\u8680\u7387\u9884\u6d4b\u3001\u6545\u969c\u8bca\u65ad\u9884\u6d4b\u3001\u5149\u4f0f\u529f\u7387\u9884\u6d4b\u3001\u8f68\u8ff9\u9884\u6d4b\u3001\u822a\u7a7a\u53d1\u52a8\u673a\u5bff\u547d\u9884\u6d4b\u3001\u6c47\u7387\u9884\u6d4b\u3001\u6df7\u51dd\u571f\u5f3a\u5ea6\u9884\u6d4b\u3001\u52a0\u70ed\u7089\u7089\u6e29\u9884\u6d4b\u3001\u4ef7\u683c\u9884\u6d4b\u3001\u4ea4\u901a\u6d41\u9884\u6d4b\u3001\u5c45\u6c11\u6d88\u8d39\u6307\u6570\u9884\u6d4b\u3001\u7a7a\u6c14\u8d28\u91cf\u9884\u6d4b\u3001\u7cae\u98df\u6e29\u5ea6\u9884\u6d4b\u3001\u6c14\u6e29\u9884\u6d4b\u3001\u6e05\u6c34\u503c\u9884\u6d4b\u3001\u5931\u4e1a\u7387\u9884\u6d4b\u3001\u7528\u7535\u91cf\u9884\u6d4b\u3001\u8fd0\u8f93\u91cf\u9884\u6d4b\u3001\u5236\u9020\u4e1a\u91c7\u8d2d\u7ecf\u7406\u6307\u6570\u9884\u6d4b<\/p>\n

3 \u56fe\u50cf\u5904\u7406\u65b9\u9762 3.1 \u56fe\u50cf\u8fb9\u7f18\u68c0\u6d4b 3.2 \u56fe\u50cf\u5904\u7406 3.3 \u56fe\u50cf\u5206\u5272 3.4 \u56fe\u50cf\u5206\u7c7b 3.5 \u56fe\u50cf\u8ddf\u8e2a 3.6 \u56fe\u50cf\u52a0\u5bc6\u89e3\u5bc6 3.7 \u56fe\u50cf\u68c0\u7d22 3.8 \u56fe\u50cf\u914d\u51c6 3.9 \u56fe\u50cf\u62fc\u63a5 3.10 \u56fe\u50cf\u8bc4\u4ef7 3.11 \u56fe\u50cf\u53bb\u566a 3.12 \u56fe\u50cf\u878d\u5408 3.13 \u56fe\u50cf\u8bc6\u522b 3.13.1 \u8868\u76d8\u8bc6\u522b 3.13.2 \u8f66\u9053\u7ebf\u8bc6\u522b 3.13.3 \u8f66\u8f86\u8ba1\u6570 3.13.4 \u8f66\u8f86\u8bc6\u522b 3.13.5 \u8f66\u724c\u8bc6\u522b 3.13.6 \u8f66\u4f4d\u8bc6\u522b 3.13.7 \u5c3a\u5bf8\u68c0\u6d4b 3.13.8 \u7b54\u9898\u5361\u8bc6\u522b 3.13.9 \u7535\u5668\u8bc6\u522b 3.13.10 \u8dcc\u5012\u68c0\u6d4b 3.13.11 \u52a8\u7269\u8bc6\u522b 3.13.12 \u4e8c\u7ef4\u7801\u8bc6\u522b 3.13.13 \u53d1\u7968\u8bc6\u522b 3.13.14 \u670d\u88c5\u8bc6\u522b 3.13.15 \u6c49\u5b57\u8bc6\u522b 3.13.16 \u7ea2\u7eff\u706f\u8bc6\u522b 3.13.17 \u8679\u819c\u8bc6\u522b 3.13.18 \u706b\u707e\u68c0\u6d4b 3.13.19 \u75be\u75c5\u5206\u7c7b 3.13.20 \u4ea4\u901a\u6807\u5fd7\u8bc6\u522b 3.13.21 \u5361\u53f7\u8bc6\u522b 3.13.22 \u53e3\u7f69\u8bc6\u522b 3.13.23 \u88c2\u7f1d\u8bc6\u522b 3.13.24 \u76ee\u6807\u8ddf\u8e2a 3.13.25 \u75b2\u52b3\u68c0\u6d4b 3.13.26 \u65d7\u5e1c\u8bc6\u522b 3.13.27 \u9752\u8349\u8bc6\u522b 3.13.28 \u4eba\u8138\u8bc6\u522b 3.13.29 \u4eba\u6c11\u5e01\u8bc6\u522b 3.13.30 \u8eab\u4efd\u8bc1\u8bc6\u522b 3.13.31 \u624b\u52bf\u8bc6\u522b 3.13.32 \u6570\u5b57\u5b57\u6bcd\u8bc6\u522b 3.13.33 \u624b\u638c\u8bc6\u522b 3.13.34 \u6811\u53f6\u8bc6\u522b 3.13.35 \u6c34\u679c\u8bc6\u522b 3.13.36 \u6761\u5f62\u7801\u8bc6\u522b 3.13.37 \u6e29\u5ea6\u68c0\u6d4b 3.13.38 \u7455\u75b5\u68c0\u6d4b 3.13.39 \u82af\u7247\u68c0\u6d4b 3.13.40 \u884c\u4e3a\u8bc6\u522b 3.13.41 \u9a8c\u8bc1\u7801\u8bc6\u522b 3.13.42 \u836f\u6750\u8bc6\u522b 3.13.43 \u786c\u5e01\u8bc6\u522b 3.13.44 \u90ae\u653f\u7f16\u7801\u8bc6\u522b 3.13.45 \u7eb8\u724c\u8bc6\u522b 3.13.46 \u6307\u7eb9\u8bc6\u522b<\/p>\n

3.14 \u56fe\u50cf\u4fee\u590d 3.15 \u56fe\u50cf\u538b\u7f29 3.16 \u56fe\u50cf\u9690\u5199 3.17 \u56fe\u50cf\u589e\u5f3a 3.18 \u56fe\u50cf\u91cd\u5efa<\/p>\n

4 \u8def\u5f84\u89c4\u5212\u65b9\u9762 4.1 \u65c5\u884c\u5546\u95ee\u9898（TSP） 4.1.1 \u5355\u65c5\u884c\u5546\u95ee\u9898（TSP） 4.1.2 \u591a\u65c5\u884c\u5546\u95ee\u9898（MTSP） 4.2 \u8f66\u8f86\u8def\u5f84\u95ee\u9898（VRP） 4.2.1 \u8f66\u8f86\u8def\u5f84\u95ee\u9898（VRP） 4.2.2 \u5e26\u5bb9\u91cf\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（CVRP） 4.2.3 \u5e26\u5bb9\u91cf+\u65f6\u95f4\u7a97+\u8ddd\u79bb\u8f66\u8f86\u8def\u5f84\u95ee\u9898（DCTWVRP） 4.2.4 \u5e26\u5bb9\u91cf+\u8ddd\u79bb\u8f66\u8f86\u8def\u5f84\u95ee\u9898（DCVRP） 4.2.5 \u5e26\u8ddd\u79bb\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（DVRP） 4.2.6 \u5e26\u5145\u7535\u7ad9+\u65f6\u95f4\u7a97\u8f66\u8f86\u8def\u5f84\u95ee\u9898（ETWVRP） 4.2.3 \u5e26\u591a\u79cd\u5bb9\u91cf\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（MCVRP） 4.2.4 \u5e26\u8ddd\u79bb\u7684\u591a\u8f66\u8f86\u8def\u5f84\u95ee\u9898（MDVRP） 4.2.5 \u540c\u65f6\u53d6\u9001\u8d27\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（SDVRP） 4.2.6 \u5e26\u65f6\u95f4\u7a97+\u5bb9\u91cf\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（TWCVRP） 4.2.6 \u5e26\u65f6\u95f4\u7a97\u7684\u8f66\u8f86\u8def\u5f84\u95ee\u9898（TWVRP） 4.3 \u591a\u5f0f\u8054\u8fd0\u8fd0\u8f93\u95ee\u9898<\/p>\n

4.4 \u673a\u5668\u4eba\u8def\u5f84\u89c4\u5212 4.4.1 \u907f\u969c\u8def\u5f84\u89c4\u5212 4.4.2 \u8ff7\u5bab\u8def\u5f84\u89c4\u5212 4.4.3 \u6805\u683c\u5730\u56fe\u8def\u5f84\u89c4\u5212<\/p>\n

4.5 \u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.1 \u51b7\u94fe\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.2 \u5916\u5356\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.3 \u53e3\u7f69\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.4 \u836f\u54c1\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.5 \u542b\u5145\u7535\u7ad9\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.6 \u8fde\u9501\u8d85\u5e02\u914d\u9001\u8def\u5f84\u89c4\u5212 4.5.7 \u8f66\u8f86\u534f\u540c\u65e0\u4eba\u673a\u914d\u9001\u8def\u5f84\u89c4\u5212<\/p>\n

4.6 \u65e0\u4eba\u673a\u8def\u5f84\u89c4\u5212 4.6.1 \u98de\u884c\u5668\u4eff\u771f 4.6.2 \u65e0\u4eba\u673a\u98de\u884c\u4f5c\u4e1a 4.6.3 \u65e0\u4eba\u673a\u8f68\u8ff9\u8ddf\u8e2a 4.6.4 \u65e0\u4eba\u673a\u96c6\u7fa4\u4eff\u771f 4.6.5 \u65e0\u4eba\u673a\u4e09\u7ef4\u8def\u5f84\u89c4\u5212 4.6.6 \u65e0\u4eba\u673a\u7f16\u961f 4.6.7 \u65e0\u4eba\u673a\u534f\u540c\u4efb\u52a1 4.6.8 \u65e0\u4eba\u673a\u4efb\u52a1\u5206\u914d<\/p>\n

5 \u8bed\u97f3\u5904\u7406 5.1 \u8bed\u97f3\u60c5\u611f\u8bc6\u522b 5.2 \u58f0\u6e90\u5b9a\u4f4d 5.3 \u7279\u5f81\u63d0\u53d6 5.4 \u8bed\u97f3\u7f16\u7801 5.5 \u8bed\u97f3\u5904\u7406 5.6 \u8bed\u97f3\u5206\u79bb 5.7 \u8bed\u97f3\u5206\u6790 5.8 \u8bed\u97f3\u5408\u6210 5.9 \u8bed\u97f3\u52a0\u5bc6 5.10 \u8bed\u97f3\u53bb\u566a 5.11 \u8bed\u97f3\u8bc6\u522b 5.12 \u8bed\u97f3\u538b\u7f29 5.13 \u8bed\u97f3\u9690\u85cf<\/p>\n

6 \u5143\u80de\u81ea\u52a8\u673a\u65b9\u9762 6.1 \u5143\u80de\u81ea\u52a8\u673a\u75c5\u6bd2\u4eff\u771f 6.2 \u5143\u80de\u81ea\u52a8\u673a\u57ce\u5e02\u89c4\u5212 6.3 \u5143\u80de\u81ea\u52a8\u673a\u4ea4\u901a\u6d41 6.4 \u5143\u80de\u81ea\u52a8\u673a\u6c14\u4f53 6.5 \u5143\u80de\u81ea\u52a8\u673a\u4eba\u5458\u758f\u6563 6.6 \u5143\u80de\u81ea\u52a8\u673a\u68ee\u6797\u706b\u707e 6.7 \u5143\u80de\u81ea\u52a8\u673a\u751f\u547d\u6e38\u620f<\/p>\n

7 \u4fe1\u53f7\u5904\u7406\u65b9\u9762 7.1 \u6545\u969c\u4fe1\u53f7\u8bca\u65ad\u5206\u6790 7.1.1 \u9f7f\u8f6e\u635f\u4f24\u8bc6\u522b 7.1.2 \u5f02\u6b65\u7535\u673a\u8f6c\u5b50\u65ad\u6761\u6545\u969c\u8bca\u65ad 7.1.3 \u6eda\u52a8\u4f53\u5185\u5916\u5708\u6545\u969c\u8bca\u65ad\u5206\u6790 7.1.4 \u7535\u673a\u6545\u969c\u8bca\u65ad\u5206\u6790 7.1.5 \u8f74\u627f\u6545\u969c\u8bca\u65ad\u5206\u6790 7.1.6 \u9f7f\u8f6e\u7bb1\u6545\u969c\u8bca\u65ad\u5206\u6790 7.1.7 \u4e09\u76f8\u9006\u53d8\u5668\u6545\u969c\u8bca\u65ad\u5206\u6790 7.1.8 \u67f4\u6cb9\u673a\u6545\u969c\u8bca\u65ad<\/p>\n

7.2 \u96f7\u8fbe\u901a\u4fe1 7.2.1 FMCW\u4eff\u771f 7.2.2 GPS\u6297\u5e72\u6270 7.2.3 \u96f7\u8fbeLFM 7.2.4 \u96f7\u8fbeMIMO 7.2.5 \u96f7\u8fbe\u6d4b\u89d2 7.2.6 \u96f7\u8fbe\u6210\u50cf 7.2.7 \u96f7\u8fbe\u5b9a\u4f4d 7.2.8 \u96f7\u8fbe\u56de\u6ce2 7.2.9 \u96f7\u8fbe\u68c0\u6d4b 7.2.10 \u96f7\u8fbe\u6570\u5b57\u4fe1\u53f7\u5904\u7406 7.2.11 \u96f7\u8fbe\u901a\u4fe1 7.2.12 \u96f7\u8fbe\u76f8\u63a7\u9635 7.2.13 \u96f7\u8fbe\u4fe1\u53f7\u5206\u6790 7.2.14 \u96f7\u8fbe\u9884\u8b66 7.2.15 \u96f7\u8fbe\u8109\u51b2\u538b\u7f29 7.2.16 \u5929\u7ebf\u65b9\u5411\u56fe 7.2.17 \u96f7\u8fbe\u6742\u6ce2\u4eff\u771f<\/p>\n

7.3 \u751f\u7269\u7535\u4fe1\u53f7 7.3.1 \u808c\u7535\u4fe1\u53f7EMG 7.3.2 \u8111\u7535\u4fe1\u53f7EEG 7.3.3 \u5fc3\u7535\u4fe1\u53f7ECG 7.3.4 \u5fc3\u810f\u4eff\u771f<\/p>\n

7.4 \u901a\u4fe1\u7cfb\u7edf 7.4.1 DOA\u4f30\u8ba1 7.4.2 LEACH\u534f\u8bae 7.4.3 \u7f16\u7801\u8bd1\u7801 7.4.4 \u53d8\u5206\u6a21\u6001\u5206\u89e3 7.4.5 \u8d85\u5bbd\u5e26\u4eff\u771f 7.4.6 \u591a\u5f84\u8870\u843d\u4eff\u771f 7.4.7 \u8702\u7a9d\u7f51\u7edc 7.4.8 \u7ba1\u9053\u6cc4\u6f0f 7.4.9 \u7ecf\u9a8c\u6a21\u6001\u5206\u89e3 7.4.10 \u6ee4\u6ce2\u5668\u8bbe\u8ba1 7.4.11 \u6a21\u62df\u4fe1\u53f7\u4f20\u8f93 7.4.12 \u6a21\u62df\u4fe1\u53f7\u8c03\u5236 7.4.13 \u6570\u5b57\u57fa\u5e26\u4fe1\u53f7 7.4.14 \u6570\u5b57\u4fe1\u9053 7.4.15 \u6570\u5b57\u4fe1\u53f7\u5904\u7406 7.4.16 \u6570\u5b57\u4fe1\u53f7\u4f20\u8f93 7.4.17 \u6570\u5b57\u4fe1\u53f7\u53bb\u566a 7.4.18 \u6c34\u58f0\u901a\u4fe1 7.4.19 \u901a\u4fe1\u4eff\u771f 7.4.20 \u65e0\u7ebf\u4f20\u8f93 7.4.21 \u8bef\u7801\u7387\u4eff\u771f 7.4.22 \u73b0\u4ee3\u901a\u4fe1 7.4.23 \u4fe1\u9053\u4f30\u8ba1 7.4.24 \u4fe1\u53f7\u68c0\u6d4b 7.4.25 \u4fe1\u53f7\u878d\u5408 7.4.26 \u4fe1\u53f7\u8bc6\u522b 7.4.27 \u538b\u7f29\u611f\u77e5 7.4.28 \u566a\u58f0\u4eff\u771f 7.4.29 \u566a\u58f0\u5e72\u6270<\/p>\n

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