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microsoft office access 2003 - the complete reference (2003)

microsoft office access 2003 - the complete reference (2003)

Kỹ thuật lập trình

... move either one, click the move handle located at the farleft edge of the bar (it looks like a stack of dots) Then, drag the bar away from the top of the window to another edge or leave it in the ... later The status bar at the bottom of the Access window displays the description of the current field included in the table definition For example, if the cursor is in the first field—Order ID the ... all the components of the database in the Database window The left pane of the Database window shows a set of buttons grouped under the Objects title button The buttons are labeled with the names...
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Tài liệu Finding, Filtering, and Sorting Rows in a DataTable ppt

Tài liệu Finding, Filtering, and Sorting Rows in a DataTable ppt

Kỹ thuật lập trình

... Notice that the Find() method is called through the Rows property of productsDataTable The Rows property returns an object of the DataRowCollection class If the primary key for the database ... database table consists of more than one column, then you can pass an array of objects to the Find() method For example, the Order Details table's primary key is made up of the OrderID and ProductID ... specifies the rows to select sortExpression specifies how the selected rows are to be ordered myDataViewRowState specifies the state of the rows to select You set myDataViewRowState to one of the constants...
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Tài liệu Filtering and Sorting Data pptx

Tài liệu Filtering and Sorting Data pptx

Kỹ thuật lập trình

... of a DataView created from the DataViewManager for the table Accessing these properties is identical to accessing the same properties directly through the DataView The RowFilter property of the ... DataView accesses the expression that filters the view The Sort property of the DataView sorts the view on a single or multiple columns in either ascending or descending order In the sample, a filter ... in the DataSet The object is accessed using the name or ordinal of the table by using an indexer in C# or by using the Item( ) property in VB.NET The DataViewSetting object allows access to the...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Hóa học - Dầu khí

... derivative of the ith component of Fðk; xÞ with respect to the jth component of x Likewise, the ijth entry of Hk is equal to the partial derivative of the ith component of Hðk; xÞ with respect to the ... the unforced dynamical behavior of the system; the subscript k denotes discrete time In other words, the state is the least amount of data on the past behavior of the system that is needed to predict ... xÀ Þ; ð1:12Þ in light of which, the matrix Gk is called the Kalman gain There now remains the problem of deriving an explicit formula for Gk Since, from the principle of orthogonality, we have...
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Tài liệu Kalman Filtering and Neural Networks P2 doc

Tài liệu Kalman Filtering and Neural Networks P2 doc

Điện - Điện tử

... contribution from the jth step of backk propagation to the computation of the total derivative matrix for the ith node; the vector ui;j is the vector of inputs to the ith node at the jth step of k backpropagation; ... set of G submatrices Hik , where G is the number of nodes of the network Then, each matrix Hik denotes the matrix of derivatives of network outputs with respect to the weights associated with the ... multiple-output network in which the number of original outputs is multiplied by the number of streams The nature of the Kalman recursion, because of the global scaling matrix Ak , is then to produce weight...
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Tài liệu Kalman Filtering and Neural Networks P3 doc

Tài liệu Kalman Filtering and Neural Networks P3 doc

Điện - Điện tử

... that the network would not learn the order of presentation of the sequences The network was therefore expected to learn the motions associated with each of the three shapes, and not the order of ... receptive fields of size  at the input are fed to the four banks of four units in the first hidden layer The second layer of eight units then combines these local features learned by the first hidden ... predict the correct shape and location of the next image in the sequence The complexity of the problem was increased from Experiment to as we introduced occlusions, increased both the length of the...
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Tài liệu Kalman Filtering and Neural Networks P4 doc

Tài liệu Kalman Filtering and Neural Networks P4 doc

Điện - Điện tử

... of the data sets used, and their division into the training and test sets, respectively Also shown is a partial summary of the dynamic invariants for each of the data sets used and the size of ... for the two signals also match very closely The theoretical horizons of predictability of the two signals are also in agreement with each other These results demonstrate very convincingly that the ... using the first 5000 samples in the same fashion as in the noise-free case The right-hand plots of Figures 4.9a and 4.9b show the attractors of the autonomously generated Ikeda series produced by the...
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Tài liệu Kalman Filtering and Neural Networks P5 pdf

Tài liệu Kalman Filtering and Neural Networks P5 pdf

Điện - Điện tử

... not otherwise available Finally, a number of examples have been presented to illustrate the performance of the dual EKF methods The ability of the dual EKF to capture the underlying dynamics of ... 5.10 Note that the dimension of the state space is in the case of variance estimation, while the observation k is generally multidimensional For this reason, the covariance form of the KF is more ... in the comparison The noise reduction is most successful in nonspeech portions of the signal, but is also apparent in the visibility of formants of the estimated signal, which are obscured in the...
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Tài liệu Kalman Filtering and Neural Networks P6 pdf

Tài liệu Kalman Filtering and Neural Networks P6 pdf

Điện - Điện tử

... Summary of the main steps of the NLDS-EM algorithm the goal of the MFA initialization is to capture the nonlinear shape of the output manifold Estimating the dynamics is difficult (since the hidden ... made at the beginning of the training procedure The first is to judiciously select the placement of the RBF kernels in the representation of the state dynamics and=or output function The second ... the observations=inputs and the parameter values The M-step involves system identification using the state estimates from the smoother Therefore, at the heart of the EM learning procedure is the...
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Tài liệu Kalman Filtering and Neural Networks P7 pptx

Tài liệu Kalman Filtering and Neural Networks P7 pptx

Điện - Điện tử

... divergence of the filter.4 It is these ‘‘flaws’’ that will be addressed in the next section using the UKF 7.3 THE UNSCENTED KALMAN FILTER The UKF addresses the approximation issues of the EKF The state ... augmented with the noise RVs This reduces the dimension of the sigma points as well as the total number of sigma points used The covariances of the noise source are then incorporated into the state ... in Table 7.3 The complexity of the algorithm is of order L3, where L is the dimension of the state This is the same complexity as the EKF The most costly operation is in forming the sample prior...
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Tài liệu Navigating Between Parent and Child Records Using a DataRelation pptx

Tài liệu Navigating Between Parent and Child Records Using a DataRelation pptx

Kỹ thuật lập trình

... specify the version of the data to retrieve as one of the DataRowVersion enumeration values: Current, Default, Original, or Proposed Similarly, the GetParentRow( ) method of a row in the child ... result.ToString( ); Discussion The GetChildRows( ) method of a row in the parent table returns the child rows as an array of DataRow objects for a specified DataRelation The method takes an optional ... child table returns the parent row as a DataRow object for a specified DataRelation Again, an optional second argument allows a specific version of the data to be returned The GetParentRows( )...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Hóa học - Dầu khí

... of the data sets used, and their division into the training and test sets, respectively Also shown is a partial summary of the dynamic invariants for each of the data sets used and the size of ... for the two signals also match very closely The theoretical horizons of predictability of the two signals are also in agreement with each other These results demonstrate very convincingly that the ... using the first 5000 samples in the same fashion as in the noise-free case The right-hand plots of Figures 4.9a and 4.9b show the attractors of the autonomously generated Ikeda series produced by the...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Hóa học - Dầu khí

... not otherwise available Finally, a number of examples have been presented to illustrate the performance of the dual EKF methods The ability of the dual EKF to capture the underlying dynamics of ... 5.10 Note that the dimension of the state space is in the case of variance estimation, while the observation k is generally multidimensional For this reason, the covariance form of the KF is more ... in the comparison The noise reduction is most successful in nonspeech portions of the signal, but is also apparent in the visibility of formants of the estimated signal, which are obscured in the...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Hóa học - Dầu khí

... Summary of the main steps of the NLDS-EM algorithm the goal of the MFA initialization is to capture the nonlinear shape of the output manifold Estimating the dynamics is difficult (since the hidden ... made at the beginning of the training procedure The first is to judiciously select the placement of the RBF kernels in the representation of the state dynamics and=or output function The second ... the observations=inputs and the parameter values The M-step involves system identification using the state estimates from the smoother Therefore, at the heart of the EM learning procedure is the...
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Tài liệu Kalman Filtering and Neural Networks - Chapter VII: THE UNSCENTED KALMAN FILTER pdf

Tài liệu Kalman Filtering and Neural Networks - Chapter VII: THE UNSCENTED KALMAN FILTER pdf

Hóa học - Dầu khí

... divergence of the filter.4 It is these ‘‘flaws’’ that will be addressed in the next section using the UKF 7.3 THE UNSCENTED KALMAN FILTER The UKF addresses the approximation issues of the EKF The state ... augmented with the noise RVs This reduces the dimension of the sigma points as well as the total number of sigma points used The covariances of the noise source are then incorporated into the state ... in Table 7.3 The complexity of the algorithm is of order L3, where L is the dimension of the state This is the same complexity as the EKF The most costly operation is in forming the sample prior...
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Tài liệu Kalman Filtering and Neural Networks - Contents pptx

Tài liệu Kalman Filtering and Neural Networks - Contents pptx

Hóa học - Dầu khí

... Chapter 1, all the other chapters present illustrative applications of the learning algorithms described here, some of which involve the use of simulated as well as real-life data Much of the material ... problem, which refers to the problem of simultaneously estimating the state of a nonlinear dynamical system and the model that gives rise to the underlying dynamics of the system  Chapter studies ... for a refined estimation of the state xi xii PREFACE  Chapter studies yet another novel idea – the unscented Kalman filter – the performance of which is superior to that of the extended Kalman filter...
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Tài liệu Add and Delete Records Using Bound Controls ppt

Tài liệu Add and Delete Records Using Bound Controls ppt

Cơ sở dữ liệu

... called When the user clicks the btnDelete button, the record is deleted from the recordset and then from the server The list box is reloaded and the first record in the list is displayed in the text ... to True, the dataset is set for adding a record with the AddNew method of the BindingContext, and the text boxes are enabled for editing If the user then clicks the btnSave button, the data is ... collapsed) Add the following code to the Click event of the new command button btnNew This code first sets the Boolean variable called mbAddNew to True It then uses the AddNew method of the form's...
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