图 书 馆
鉴定，估计和学习Identification, Estimation, and Learning |
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| 课程网址: | http://ocw.mit.edu/courses/mechanical-engineering/2-160-identific... |
| 主讲教师: | Prof. Harry Asada |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2006-01-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation. |
| 关 键 词: | 系统辨识; 卡尔曼滤波; 噪音动态; 系统表示; 函数逼近论; 神经网络; 径向基函数; 翔实的数据集 |
| 课程来源: | 麻省理工学院公开课 |
| 最后编审: | 2020-09-28：yumf |
| 阅读次数: | 67 |