The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2. The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples. The returned model has the form 1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2) 2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2) 看不懂，据高手说，非常有用。
标签： identification neuro-fuzzy implemented analysis
上传时间： 2014-01-12
上传用户：zgu489
Feature List and Tutorial Manual Version 6.1很好的教材和教辅
标签： Tutorial Feature Version Manual
上传时间： 2013-12-15
上传用户：aig85
Nonparametric Identification Of A Particular Nonlinear Time Series System
标签： Identification Nonparametric Particular Nonlinear
上传时间： 2014-11-30
上传用户：515414293
Enhance Extened Display Identification data Standard
标签： Identification Standard Enhance Display
上传时间： 2016-06-14
上传用户：lhc9102
Single Neural Net PID Controller based on RBF Identification using matlab
标签： Identification Controller Single Neural
上传时间： 2014-11-29
上传用户：ddddddos
開平方根IP將sqroot_license.txt中的FEATURE 6AF8_0048 alterad 0000.00 permanent uncounted 4A689178551B VENDOR_STRING=gl15kdhm5gUPkJD7iM82mn$$ HOSTID=ANY加入就可以使用了！
标签： sqroot_license 4A689178551B permanent uncounted
上传时间： 2016-08-17
上传用户：windwolf2000
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
标签： identification considered features separati
上传时间： 2016-09-20
上传用户：FreeSky
A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction. KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
标签： identification time-invariant presented emphasis
上传时间： 2013-12-28
上传用户：wfl_yy
自已写的Adaboost使用Haalike feature，去Detection
上传时间： 2016-09-21
上传用户：jennyzai
Feature Extraction of Infrared Target Based on Image Moment and Wavelet Energy
标签： Extraction Infrared Feature Wavelet
上传时间： 2016-11-12
上传用户：BOBOniu
