least square estimation of system identification
標簽： identification estimation square system
上傳時間： 2014-01-08
上傳用戶：Shaikh
Please read your package and describe it at least 40 bytes in English. System will automatically delete the directory of debug and release, so please do not put files on these two directory.
標簽： automatically describe English package
上傳時間： 2017-08-29
上傳用戶：ccclll
Mean shift clustering. K means clustering.
標簽： clustering shift means Mean
上傳時間： 2014-01-08
上傳用戶：釣鰲牧馬
f you have not registered, Please [regist first].You should upload at least five sourcecodes/documents. (upload 5 files, you can download 200 files). Webmaster will activate your member account after checking your files. If you do not want to upload source code, you can join the [VIP member] to
標簽： sourcecodes registered documen Please
上傳時間： 2017-09-13
上傳用戶：ljmwh2000
f you have not registered, Please [regist first].You should upload at least five sourcecodes/documents. (upload 5 files, you can download 200 files). Webmaster will activate your member account after checking your files. If you do not want to upload source code, you can join the [VIP member] to
標簽： sourcecodes registered documen Please
上傳時間： 2014-01-16
上傳用戶：fandeshun
Please read your package and describe it at least 40 bytes in English.
標簽： describe English package Please
上傳時間： 2013-12-06
上傳用戶：waizhang
Please read your package and describe it at least 40 bytes in English. System will automatically delete the directory of debug and release, so please do not put files on these two directory.
標簽： automatically describe English package
上傳時間： 2017-09-20
上傳用戶：alan-ee
a function called fit least square in mathematics with an excel sheet
標簽： mathematics function called square
上傳時間： 2014-08-12
上傳用戶：yiwen213
最小二乘法曲面擬合,包括C程序及說明文件。對于搞三維重建的有一定幫助-Least squares surface fitting, including the C procedures and documentation. For engaging in three-dimensional reconstruction to some extent help the
標簽： 通信網
上傳時間： 2015-11-28
上傳用戶：schhqq
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標簽： 傳感器網絡
上傳時間： 2016-11-27
上傳用戶：xxmluo
