In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning

<div>MMSE value and cosine similarity evaluation results of 7 kinds of loss function corresponding to test set. Unlike the loss function of the cosine similarity, the similarity value here is the similarity coefficient distributed in <span class="nowrap"><svg height="11.439pt" id="M13" style="vertical-align:-2.15067pt" version="1.1" viewbox="-0.0498162 -9.28833 34.4439 11.439" width="34.4439pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M290 -163V-135C183 -126 181 -122 181 -44V583C181 662 184 666 290 675V703H120V-163H290Z"></path></g><g transform="matrix(.013,0,0,-0.013,4.485,0)"><path d="M535 230V280H52V230H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,12.116,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,18.356,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z"></path></g><g transform="matrix(.013,0,0,-0.013,23.499,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,29.739,0)"><path d="M226 -163V703H56V676C162 667 165 662 165 584V-43C165 -122 162 -126 56 -136V-163H226Z"></path></g></svg>,</span> where 1 represents completely similar and −1 represents completely opposite.</div>

Complexity

fig7

Figure 7

Figure 7: In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning 