The design of Kalman filter for estimation of state
標(biāo)簽： estimation Kalman design filter
上傳時(shí)間： 2013-12-23
上傳用戶：gtf1207
A 8 puzzle program solver.user have to input the current state and goal state of your 8 puzzle and it solving by the program.
標(biāo)簽： puzzle state and program
上傳時(shí)間： 2014-08-10
上傳用戶：huyiming139
java vending Machine source code
標(biāo)簽： Machine vending source java
上傳時(shí)間： 2017-07-23
上傳用戶：aix008
This is a support vector machine program developed based on quadprog. Polynomial and RBF kernel are supported. Test it by executing example.m with supported data.
標(biāo)簽： Polynomial developed quadprog support
上傳時(shí)間： 2014-01-04
上傳用戶：ddddddos
list of matlab m-files on matlab 7.0. learning , support vector machine and some utility routines : autocorrelation, linearly scale randomize the row order of a matrix
標(biāo)簽： matlab learning routines m-files
上傳時(shí)間： 2017-07-24
上傳用戶：evil
an introduction for support vector machine
標(biāo)簽： introduction support machine vector
上傳時(shí)間： 2017-07-24
上傳用戶：集美慧
a good document that introduces support vector machine in detail and covers almost all of contents we need generally
標(biāo)簽： introduces document contents support
上傳時(shí)間： 2014-01-10
上傳用戶：450976175
Two scripts are included here. 1. convsys.m - combines the state space representation of two systems connected in series. [Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2) This algorithm gives the convolution of two state space representations | A1 B1 | | A2 B2 | u ==> | | ==> | | ==> y | C1 D1 | | C2 D2 | The algorithm also accepts state space objects as inputs and gives out a state space object as output. 2. sysfeedbk.m [Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2) Gives the closed loop state space representation for two systems connected with negative feedback in the following manner. | A1 B1 | u ==> | | ==> y + o | C1 D1 | | - | | | | A2 B2 | | |= | |= | | C2 D2 | The zip file also contains checkcompatibility.m , which checks the compatibility of matrix dimensions in the system and cleanss.m which can be used to clean a state space representation.
標(biāo)簽： representation included combines scripts
上傳時(shí)間： 2017-07-25
上傳用戶：semi1981
機(jī)器學(xué)習(xí)+Tom+M.+Mitchell《Machine+Learning》之中文版。據(jù)我們導(dǎo)師說(shuō)這是一本很好的人工智能方面的書(shū)，希望學(xué)習(xí)人工智能的可以看看,我剛找到看。
標(biāo)簽： Mitchell Learning Machine Tom
上傳時(shí)間： 2014-01-15
上傳用戶：天涯
The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory.
標(biāo)簽： filter efficient estimates recursive
上傳時(shí)間： 2017-08-06
上傳用戶：風(fēng)之驕子
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