{"id":52163,"date":"2024-08-20T18:47:09","date_gmt":"2024-08-20T10:47:09","guid":{"rendered":"https:\/\/swarma.org\/?p=52163"},"modified":"2024-08-20T18:47:09","modified_gmt":"2024-08-20T10:47:09","slug":"nat-commun-%e5%89%8d%e6%b2%bf%ef%bc%9a%e5%a4%9a%e5%b0%ba%e5%ba%a6%e5%82%a8%e5%a4%87%e6%b1%a0%e8%ae%a1%e7%ae%97%e5%ad%a6%e4%b9%a0%e5%99%aa%e9%9f%b3%e8%af%b1%e5%af%bc%e7%9a%84%e9%9a%8f%e6%9c%ba","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=52163","title":{"rendered":"Nat. Commun.\u524d\u6cbf\uff1a\u591a\u5c3a\u5ea6\u50a8\u5907\u6c60\u8ba1\u7b97\u5b66\u4e60\u566a\u97f3\u8bf1\u5bfc\u7684\u968f\u673a\u8f6c\u79fb"},"content":{"rendered":"
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\u8bba\u6587\u9898\u76ee\uff1aLearning noise-induced transitions by multi-scaling reservoir computing<\/span><\/section>\n
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\u56fe1\uff1a\u591a\u5c3a\u5ea6\u5316\u50a8\u5907\u6c60\u8ba1\u7b97\u7684\u6846\u67b6\u3002(a) \u5feb\u6162\u5c3a\u5ea6\u6df7\u5408\u7684\u968f\u673a\u65f6\u95f4\u5e8f\u5217\uff0c\u5176\u4e2d\u5feb\u5c3a\u5ea6\u7684\u90e8\u5206\u53ef\u4ee5\u88ab\u89c6\u4e3a\u566a\u58f0\uff0c\u5bfc\u81f4\u7a33\u5b9a\u72b6\u6001\u4e4b\u95f4\u7684\u566a\u58f0\u8bf1\u5bfc\u8f6c\u6362\u3002\u5728\u8bad\u7ec3\u9636\u6bb5\uff0c\u8f93\u51fa\u77e9\u9635Wout<\/sub>\u88ab\u8bad\u7ec3\u3002\u8c03\u6574\u8d85\u53c2<\/span>\u6570<\/span>\u03b1<\/span>\u53ef\u4ee5<\/span>\u6539\u53d8\u8f93\u51fa\u7684\u65f6\u95f4\u5c3a\u5ea6\uff0c\u9002\u5f53\u9009\u62e9\u7684\u03b1<\/span>\u53ef\u4ee5\u4f7f\u8f93\u51fa\u4e0e\u6162\u5c3a\u5ea6\u52a8\u529b\u5b66\u76f8\u5339\u914d\u3002\u7136\u540e\u5c06\u5feb\u901f\u65f6\u95f4\u5c3a\u5ea6\u4fe1\u53f7\u03b7<\/span>\u5206\u79bb\u4e3a\u566a\u58f0\u65f6\u95f4\u5e8f\u5217\u6216\u5206\u5e03\u3002(b) \u5728\u9884\u6d4b\u9636\u6bb5\uff0c<\/sub><\/span>\u8f93\u5165\u5230\u8bad\u7ec3\u597d\u7684\u50a8\u5907\u6c60\u4e2d\u4ee5\u751f\u6210<\/sub><\/span>\u3002\u5728\u4e0b\u4e00\u4e2a\u65f6\u95f4\u6b65s+1<\/strong>\uff0c\u8f93\u5165<\/sub><\/span>\u662f<\/sub><\/span>\u52a0\u4e0a\u4ece\u5206\u79bb\u51fa\u7684\u566a\u58f0\u5206\u5e03\u4e2d\u91c7\u6837\u7684\u566a\u58f0\u03b7s+1<\/sub><\/span>\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u79f0\u4e3a\u6eda\u52a8\u9884\u6d4b\u8fed\u4ee3\u8fdb\u884c\uff0c\u4ee5\u751f\u6210\u9884\u6d4b\u65f6\u95f4\u5e8f\u5217<\/sub><\/span>\u3002(c) \u5bf9\u9884\u6d4b\u8f6c\u6362\u7edf\u8ba1\u7684\u8bc4\u4f30\u3002\u4e2d\u95f4\u5904<\/sub><\/span>\u7684\u4e0d\u540c\u989c\u8272\u7ebf\u6761\u4ee3\u8868\u91cd\u590d\u591a\u6b21\u7684\u9884\u6d4b\u3002\u901a\u8fc7\u8f6c\u6362\u65f6\u95f4\u548c\u8f6c\u6362\u6b21\u6570\u7684\u7edf\u8ba1\u91cf\u6765\u8bc4\u4f30\u51c6\u786e\u6027\u3002PDF\uff1a\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u3002<\/span><\/p>\n


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2. \u591a\u5c3a\u5ea6\u5316\u50a8\u5907\u6c60\u8ba1\u7b97\u6846\u67b6<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u4f5c\u8005\u5f00\u53d1\u4e86\u4e00\u79cd\u591a\u5c3a\u5ea6\u50a8\u5907\u6c60\u8ba1\u7b97\u6846\u67b6\uff0c\u7528\u4e8e\u5728\u65e0\u6a21\u578b\u7684\u60c5\u51b5\u4e0b\u5b66\u4e60\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8<\/strong>\u3002\u8be5\u65b9\u6cd5\u7684\u7075\u611f\u6765\u81ea\u4e8e\u7814\u7a76\u53d1\u73b0\u50a8\u5907\u6c60\u4e2d\u7684\u8d85\u53c2\u6570\u51b3\u5b9a\u4e86\u50a8\u5907\u6c60\u52a8\u529b\u5b66\u7684\u65f6\u95f4\u5c3a\u5ea6 [13]\u3002\u9274\u4e8e\u591a\u5c3a\u5ea6\u65f6\u95f4\u5e8f\u5217\uff0c\u53ef\u4ee5\u8c03\u8282\u8d85\u53c2\u6570\u4ee5\u5339\u914d\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\u3002\u5f53\u50a8\u5907\u6c60\u901a\u8fc7\u62df\u5408\u8f93\u51fa\u5c42\u77e9\u9635\u6355\u6349\u5230\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\u540e\uff0c\u53ef\u4ee5\u5c06\u5feb\u65f6\u95f4\u5c3a\u5ea6\u7684\u5e8f\u5217\u5206\u79bb\u4e3a\u4e00\u4e2a\u566a\u58f0\u5206\u5e03\u3002\u5728\u9884\u6d4b\u9636\u6bb5\uff0c\u4f5c\u8005\u5229\u7528\u8bad\u7ec3\u597d\u7684\u50a8\u5907\u6c60\u8ba1\u7b97\u673a\u6765\u6a21\u62df\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\uff0c\u7136\u540e\u5c06\u4ece\u5206\u79bb\u7684\u566a\u58f0\u5206\u5e03\u4e2d\u91c7\u6837\u7684\u566a\u58f0<\/span>\uff08\u5bf9\u4e8e\u767d\u566a\u58f0\uff09<\/span>\u6216\u4ece\u7b2c\u4e8c\u4e2a\u50a8\u5907\u6c60\u4e2d\u5b66\u4e60\u7684\u566a\u58f0<\/span>\uff08\u5bf9\u4e8e\u5f69\u8272\u566a\u58f0\uff09<\/span>\u52a0\u56de\u3002\u6574\u4e2a\u8fc7\u7a0b\u5728\u65f6\u95f4\u70b9\u4e0a\u8fed\u4ee3\uff0c\u79f0\u4e3a\u6eda\u52a8\u9884\u6d4b\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5f53\u524d\u65b9\u6cd5\u4e0e\u4e4b\u524d\u5c06\u566a\u58f0\u4ec5\u89c6\u4e3a\u5e72\u6270\u56e0\u7d20\u7684\u65b9\u6cd5\u6709\u6240\u4e0d\u540c\u3002<\/span><\/section>\n

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3. \u4ee3\u8868\u6027\u793a\u4f8b\u7684\u5e94\u7528<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u56fe2\uff1a\u4ece\u4e00\u4e2a\u542b\u767d\u566a\u97f3\u7684\u4e00\u7ef4\u53cc\u7a33\u7cfb\u7edf\u4e2d\u63d0\u53d6\u968f\u673a\u8f6c\u79fb (a) \u542b\u6709\u767d\u566a\u97f3\u7684\u4e00\u7ef4\u53cc\u7a33\u7cfb\u7edf\u4e2d\u968f\u673a\u8f6c\u79fb\u7684\u793a\u610f\u56fe\u3002(b) \u4ece\u6b63\u6587\u4e2dEq. (9) (b=5, c=0, \u03b5=0.3, u1<\/sub>(0)=1.5, \u03b4<\/span>t=0.01) \u751f\u6210\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u6b64\u5904\u957f\u4e3a t=30. (c) \u8bad\u7ec3\u5f97\u5230\u7684\u6162\u65f6\u95f4\u5c3a\u5ea6\u6a21\u578b\u5c0610\u4e2a\u4e0d\u540c\u8d77\u70b9\u8f6c\u6362\u4e3a\u5341\u6761\u4e0d\u540c\u7684\u65f6\u95f4\u5e8f\u5217\uff08\u5f69\u8272\u7ebf\uff09\u6536\u655b\u5230\u5bf9\u5e94\u7684\u7a33\u6001\u5e76\u4e14\u5206\u79bb\u51fa\u566a\u97f3\u5206\u5e03\u3002(d) \u5728t\u2208<\/span>[100, 130]\u7684\u9884\u6d4b\u6570\u636e\u3002(e) \u6d4b\u8bd5\u548c\u9884\u6d4b\u6570\u636e\u4e2d\u7684\u8f6c\u79fb\u6b21\u6570\u5339\u914d\u3002\u8f6c\u79fb\u4ee3\u8868\u4eceu<\/span>1<\/sub>=-1\u5230u<\/span>1<\/sub>=1\u7684\u8f6c\u6362\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002\u9884\u6d4b\u7684\u957f\u5ea6\u662f10000\u03b4<\/span>t<\/span> (f) \u6d4b\u8bd5\u548c\u9884\u6d4b\u6570\u636e\u8f6c\u79fb\u65f6\u95f4\u7684\u76f4\u65b9\u56fe\uff0c\u8f6c\u79fb\u65f6\u95f4\u4ee3\u8868\u4e24\u6b21\u8fde\u7eed\u8f6c\u79fb\u4e4b\u95f4\u7684\u95f4\u9694\u3002<\/span><\/p>\n


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\u4e3a\u4e86\u8bc1\u660e\u5f53\u524d\u65b9\u6cd5\u7684\u6709\u6548\u6027\uff0c\u4f5c\u8005\u5c06\u5176\u5e94\u7528\u4e8e\u4e24\u7c7b\u573a\u666f\u3002\u7b2c\u4e00\u7c7b\u573a\u666f\u7684\u6570\u636e\u6765\u81ea\u4e8e\u968f\u673a\u5fae\u5206\u65b9\u7a0b<\/span> (SDE)<\/span>\uff0c\u7528\u4e8e\u6d4b\u8bd5\u8be5\u65b9\u6cd5\uff1b\u7b2c\u4e8c\u7c7b\u573a\u666f\u7684\u6570\u636e\u5219\u662f\u5b9e\u9a8c\u6570\u636e [6]\u3002\u5bf9\u4e8e\u7b2c\u4e00\u7c7b\u573a\u666f\u4e2d\u7684\u767d\u566a\u58f0\uff0c\u5305\u542b\u4e00\u7ef4\uff081D0\u53cc\u7a33\u68af\u5ea6\u7cfb\u7edf\u3001\u4e8c\u7ef4 (2D) \u53cc\u7a33\u68af\u5ea6\u548c\u975e\u68af\u5ea6\u7cfb\u7edf\u3001\u5177\u6709\u503e\u659c\u52bf\u7684\u4e00\u7ef4\u548c\u4e8c\u7ef4\u68af\u5ea6\u7cfb\u7edf\u3001\u4e8c\u7ef4\u503e\u659c\u975e\u68af\u5ea6\u7cfb\u7edf\u4ee5\u53ca\u4e8c\u7ef4\u4e09\u7a33\u7cfb\u7edf\u3002\u8be5\u65b9\u6cd5\u80fd\u591f\u6355\u6349\u8f6c\u53d8\u65f6\u95f4\u7684\u7edf\u8ba1\u7279\u5f81\u548c\u8f6c\u53d8\u6b21\u6570\u3002\u5bf9\u4e8e\u7b2c\u4e00\u7c7b\u573a\u666f\u4e2d\u7684\u5f69\u8272\u566a\u58f0\uff0c\u4f5c\u8005\u7814\u7a76\u4e86\u5177\u6709Lorenz\u566a\u58f0<\/span>\uff08Lorenz-63\u6a21\u578b\u548cLorenz-96\u6a21\u578b [12]\uff09<\/span>\u7684\u53cc\u7a33\u68af\u5ea6\u7cfb\u7edf\uff0c\u80fd\u591f\u5728\u4e0d\u5047\u8bbe\u77e5\u9053\u52a8\u529b\u5b66\u786e\u5b9a\u6027\u90e8\u5206\u7684\u524d\u63d0\u4e0b\u51c6\u786e\u9884\u6d4b\u7279\u5b9a\u7684\u8f6c\u53d8\u65f6\u95f4\uff0c\u8fd9\u533a\u522b\u4e8e [12] \u4e2d\u7684\u8981\u6c42\u3002\u5bf9\u4e8e\u7b2c\u4e8c\u7c7b\u573a\u666f\uff0c\u4f5c\u8005\u5c06\u8be5\u65b9\u6cd5\u5e94\u7528\u4e8e\u86cb\u767d\u8d28\u6298\u53e0\u6570\u636e\uff0c\u5e76\u63a2\u7d22\u4e86\u51c6\u786e\u8bad\u7ec3\u6240\u9700\u7684\u6700\u5c11\u6570\u636e\u91cf\uff0c\u8fd9\u6709\u52a9\u4e8e\u51cf\u5c11\u5b9e\u9a8c\u4e2d\u5bf9\u5e7f\u6cdb\u6d4b\u91cf\u7684\u9700\u6c42\u3002<\/span><\/section>\n

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\u8fd9\u91cc\u5c55\u793a\u4e86\u8be5\u65b9\u6cd5\u5728\u90e8\u5206\u4f8b\u5b50\u4e2d\u7684\u7ed3\u679c\uff0c\u5305\u62ec\u542b\u767d\u566a\u97f3\u7684\u4e00\u7ef4\u53cc\u7a33\u6001\u7cfb\u7edf<\/span>\uff08\u56fe2\uff09<\/span>\u548c\u5b9e\u9a8c\u4e0a\u7684\u86cb\u767d\u8d28\u7aef\u5230\u7aef\u957f\u5ea6\u6570\u636e<\/span>\uff08\u56fe3\uff09<\/span>\u7684\u7ed3\u679c\u3002<\/span><\/section>\n

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4. \u603b\u7ed3<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u603b\u7684\u6765\u8bf4\uff0c\u4f5c\u8005\u63d0\u4f9b\u4e86\u4e00\u4e2a\u57fa\u4e8e\u968f\u673a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u901a\u7528\u6846\u67b6\uff0c\u7528\u4e8e\u5b66\u4e60\u566a\u58f0\u8bf1\u5bfc\u7684\u968f\u673a\u8f6c\u6362\u3002\u4f5c\u8005\u5c06\u8fd9\u79cd\u65b9\u6cd5\u5e94\u7528\u4e8e\u968f\u673a\u5fae\u5206\u65b9\u7a0b\u548c\u5b9e\u9a8c\u6570\u636e\u7684\u4f8b\u5b50\u4e2d\uff0c\u8be5\u65b9\u6cd5\u80fd\u591f\u4ece\u5c0f\u89c4\u6a21\u7684\u8bad\u7ec3\u96c6\u4e2d\u51c6\u786e\u5b66\u4e60\u8f6c\u6362\u7edf\u8ba1\u91cf\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u62d3\u5c55SINDy\u7b97\u6cd5\u548cFORCE\u5b66\u4e60\u65b9\u6cd5\u7684\u6846\u67b6\uff0c\u6df1\u5316\u5bf9\u566a\u58f0\u8bf1\u5bfc\u73b0\u8c61\u7684\u8ba4\u8bc6\u548c\u5e94\u7528\u3002<\/span><\/section>\n

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https:\/\/github.com\/Machine-learning-and-complex-systems\/NIT-RC<\/span><\/section>\n
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\u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/section>\n
[1] R. Tapia-Rojo, M. Mora, S. Board, J. Walker, R. Boujemaa-Paterski, O. Medalia, and S. Garcia-Manyes, Enhanced statistical sampling reveals microscopic complexity in the talin mechanosensor folding energy landscape, Nat. Phys. 19, 52 (2023).<\/span><\/section>\n
[2] S. L. Brunton, J. L. Proctor, and J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proc. Natl. Acad. Sci. 113, 3932 (2016).<\/span><\/section>\n
[3] K. Kaheman, S. L. Brunton, and J. N. Kutz, Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data, Mach. Learn.: Sci. Technol. 3, 015031 (2022).<\/span><\/section>\n
[4] S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9, 1735 (1997). [5] R. R. Labbe, Filterpy documentation (2018).<\/span><\/section>\n
[6] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., Scipy 1.0: fundamental algorithms for scientific computing in python, Nat. Methods 17, 261 (2020).<\/span><\/section>\n
[7] D. Sussillo and L. F. Abbott, Generating coherent patterns of activity from chaotic neural networks, Neuron 63, 544 (2009).<\/span><\/section>\n
[8] L. B. Liu, A. Losonczy, and Z. Liao, tension: A python package for force learning, PLOS Comput. Biol. 18, e1010722 (2022).<\/span><\/section>\n
[9] H. Jaeger, The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note, Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148, 13 (2001).<\/span><\/section>\n
[10] J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach, Phys. Rev. Lett. 120, 024102 (2018).<\/span><\/section>\n
[11] T. L. Carroll, Using reservoir computers to distinguish chaotic signals, Phys. Rev. E 98, 052209 (2018).<\/span><\/section>\n
[12] S. H. Lim, L. Theo Giorgini, W. Moon, and J. S. Wettlaufer, Predicting critical transitions in multiscale dynamical systems using reservoir computing, Chaos 30, 123126 (2020).<\/span><\/section>\n
[13] G. Tanaka, T. Matsumori, H. Yoshida, and K. Aihara, Reservoir computing with diverse timescales for prediction of multiscale dynamics, Phys. Rev. Res. 4, L032014 (2022).<\/span><\/section>\n

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\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u8bfb\u4e66\u4f1a<\/strong>
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\u201c\u590d\u6742\u4e16\u754c\uff0c\u7b80\u5355\u89c4\u5219\u201d\u3002<\/span><\/p>\n


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\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u590d\u65e6\u5927\u5b66\u667a\u80fd\u590d\u6742\u4f53\u7cfb\u5b9e\u9a8c\u5ba4\u9752\u5e74\u7814\u7a76\u5458\u6731\u7fa4\u559c\u3001\u6d59\u6c5f\u5927\u5b66\u767e\u4eba\u8ba1\u5212\u7814\u7a76\u5458\u674e\u6a35\u98ce\u3001\u6e05\u534e\u5927\u5b66\u7535\u5b50\u5de5\u7a0b\u7cfb\u6570\u636e\u79d1\u5b66\u4e0e\u667a\u80fd\u5b9e\u9a8c\u5ba4\u535a\u58eb\u540e\u7814\u7a76\u5458\u4e01\u749f\u97ec\u3001\u7f8e\u56fd\u4e1c\u5317\u5927\u5b66\u7269\u7406\u7cfbAlbert-L\u00e1szl\u00f3 Barab\u00e1si\u6307\u5bfc\u7684\u535a\u58eb\u540e\u9ad8\u5a77\u5a77\u3001\u5317\u4eac\u5927\u5b66\u535a\u96c5\u535a\u58eb\u540e\u66f9\u6587\u797a\u3001\u590d\u65e6\u5927\u5b66\u6570\u5b66\u79d1\u5b66\u5b66\u9662\u5e94\u7528\u6570\u5b66\u65b9\u5411\u535a\u58eb\u7814\u7a76\u751f\u8d75\u4f2f\u6797\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u535a\u58eb\u7814\u7a76\u751f\u725f\u7267\u4e91\uff0c\u5171\u540c\u53d1\u8d77\u300c\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u300d\u8bfb\u4e66\u4f1a\u7b2c\u4e8c\u5b63<\/a>\u3002<\/span><\/p>\n


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\u8bfb\u4e66\u4f1a\u5c06\u4e8e9\u67087\u65e5\u6bcf\u5468\u516d\u665a\u4e0a20:00-22:00\u8fdb\u884c\uff0c\u63a2\u8ba8\u56db\u4e2a\u6838\u5fc3\u6a21\u5757\uff1a\u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\u3001\u590d\u6742\u7f51\u7edc\u7ed3\u6784\u63a8\u65ad\u3001\u5177\u6709\u53ef\u89e3\u91ca\u6027\u7684\u590d\u6742\u7cfb\u7edf\u63a8\u65ad\uff08\u52a8\u529b\u5b66+\u7f51\u7edc\u7ed3\u6784\uff09\u3001\u5e94\u7528-\u8d85\u6750\u6599\u8bbe\u8ba1\u548c\u57ce\u5e02\u7cfb\u7edf\uff0c\u901a\u8fc7\u91cd\u70b9\u8ba8\u8bba75\u7bc7\u7ecf\u5178\u3001\u524d\u6cbf\u7684\u91cd\u8981\u6587\u732e\uff0c\u4ece\u9ed1\u76d2\uff08\u6570\u636e\u9a71\u52a8\uff09\u5230\u767d\u76d2\uff08\u53ef\u89e3\u91ca<\/span>\u6027\uff09\uff0c\u9010\u6b65\u6355\u6349\u7cfb\u7edf\u7684\u201c\u672c\u8d28\u201d\u89c4\u5f8b\uff0c\u5e2e\u52a9\u5927\u5bb6\u66f4\u597d\u7684\u8ba4\u8bc6\u3001\u7406\u89e3\u3001\u9884\u6d4b\u3001\u63a7\u5236\u3001\u8bbe\u8ba1\u590d\u6742\u7cfb\u7edf\uff0c\u4e3a\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u548c\u5e94\u7528\u63d0\u4f9b\u6d1e\u89c1\u3002\u6b22\u8fce\u611f\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\uff01<\/span><\/p>\n

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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n

\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u8bfb\u4e66\u4f1a\uff1a\u4ece\u6570\u636e\u9a71\u52a8\u5230\u53ef\u89e3\u91ca\u6027\uff0c\u63a2\u7d22\u7cfb\u7edf\u5185\u5728\u89c4\u5f8b\uff5c\u5185\u964475\u7bc7\u9886\u57df\u5fc5\u8bfb\u6587\u732e<\/strong><\/a>
<\/p>\n


<\/section>\n

<\/section>\n
\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/strong><\/section>\n
1. <\/strong>\u524d\u6cbf\u901f\u9012\uff1a\u6f14\u5316\u7684\u50a8\u5907\u6c60\u8ba1\u7b97\u673a\u63ed\u793a\u9884\u6d4b\u6027\u80fd\u548c\u6d8c\u73b0\u52a8\u529b\u5b66\u7684\u53cc\u5411\u8026\u5408<\/a><\/strong><\/span><\/section>\n
2. <\/strong>\u590d\u6742\u7cfb\u7edf\u7684\u6570\u636e\u9a71\u52a8\u5efa\u6a21\uff1a\u50a8\u5907\u6c60\u8ba1\u7b97\u6559\u7a0b<\/a><\/strong><\/span><\/section>\n
3. <\/strong>\u901f\u9012\uff1aRNN\u53ef\u5728\u52a0\u5165\u767d\u566a\u97f3\u540e\u5b66\u5230\u6570\u636e\u5408\u9002\u7684\u9690\u85cf\u7ef4\u5ea6<\/a><\/strong><\/span><\/section>\n
4. <\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
<\/strong>5.<\/strong> \u9f99\u5e74\u5927\u8fd0\u8d77\uff0c\u5b66\u4e60\u6b63\u5f53\u65f6\uff01\u89e3\u9501\u96c6\u667a\u5168\u7ad9\u5185\u5bb9\uff0c\u5f00\u542f\u65b0\u5e74\u5b66\u4e60\u8ba1\u5212<\/a><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/strong><\/section>\n

6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/p>\n


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\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/span><\/section>\n<\/p>\n

<\/mp-style-type><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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