... move either one, click the move handle located at the farleft edge ofthe bar (it looks like a stack of dots) Then, drag the bar away from the top ofthe window to another edge or leave it in the ... later The status bar at the bottom ofthe Access window displays the description ofthe current field included in the table definition For example, if the cursor is in the first field—Order ID the ... all the components ofthe database in the Database window The left pane ofthe Database window shows a set of buttons grouped under the Objects title button The buttons are labeled with the names...
... Notice that the Find() method is called through the Rows property of productsDataTable The Rows property returns an object ofthe 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 ofthe OrderID and ProductID ... specifies the rows to select sortExpression specifies how the selected rows are to be ordered myDataViewRowState specifies the state ofthe rows to select You set myDataViewRowState to one ofthe constants...
... 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 ofthe ... DataView accesses the expression that filters the view The Sort property ofthe 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 ofthe table by using an indexer in C# or by using the Item( ) property in VB.NET The DataViewSetting object allows access to the...
... derivative ofthe 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 ofthe ith component of Hðk; xÞ with respect to the ... the unforced dynamical behavior ofthe system; the subscript k denotes discrete time In other words, the state is the least amount of data on the past behavior ofthe 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...
... contribution from the jth step of backk propagation to the computation ofthe 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 ofthe 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 ofthe Kalman recursion, because ofthe global scaling matrix Ak , is then to produce weight...
... that the network would not learn the order of presentation ofthe sequences The network was therefore expected to learn the motions associated with each ofthe 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 ofthe next image in the sequence The complexity ofthe problem was increased from Experiment to as we introduced occlusions, increased both the length of the...
... ofthe data sets used, and their division into the training and test sets, respectively Also shown is a partial summary ofthe dynamic invariants for each ofthe data sets used and the size of ... for the two signals also match very closely The theoretical horizons of predictability ofthe 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 ofthe autonomously generated Ikeda series produced by the...
... not otherwise available Finally, a number of examples have been presented to illustrate the performance ofthe dual EKF methods The ability ofthe dual EKF to capture the underlying dynamics of ... 5.10 Note that the dimension ofthe state space is in the case of variance estimation, while the observation k is generally multidimensional For this reason, the covariance form ofthe KF is more ... in the comparison The noise reduction is most successful in nonspeech portions ofthe signal, but is also apparent in the visibility of formants ofthe estimated signal, which are obscured in the...
... Summary ofthe main steps ofthe NLDS-EM algorithm the goal ofthe MFA initialization is to capture the nonlinear shape ofthe output manifold Estimating the dynamics is difficult (since the hidden ... made at the beginning ofthe training procedure The first is to judiciously select the placement ofthe RBF kernels in the representation ofthe 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 ofthe EM learning procedure is the...
... divergence ofthe 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 ofthe EKF The state ... augmented with the noise RVs This reduces the dimension ofthe sigma points as well as the total number of sigma points used The covariances ofthe noise source are then incorporated into the state ... in Table 7.3 The complexity ofthe algorithm is of order L3, where L is the dimension ofthe state This is the same complexity as the EKF The most costly operation is in forming the sample prior...
... specify the version ofthe data to retrieve as one ofthe 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 ofthe data to be returned The GetParentRows( )...
... ofthe data sets used, and their division into the training and test sets, respectively Also shown is a partial summary ofthe dynamic invariants for each ofthe data sets used and the size of ... for the two signals also match very closely The theoretical horizons of predictability ofthe 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 ofthe autonomously generated Ikeda series produced by the...
... not otherwise available Finally, a number of examples have been presented to illustrate the performance ofthe dual EKF methods The ability ofthe dual EKF to capture the underlying dynamics of ... 5.10 Note that the dimension ofthe state space is in the case of variance estimation, while the observation k is generally multidimensional For this reason, the covariance form ofthe KF is more ... in the comparison The noise reduction is most successful in nonspeech portions ofthe signal, but is also apparent in the visibility of formants ofthe estimated signal, which are obscured in the...
... Summary ofthe main steps ofthe NLDS-EM algorithm the goal ofthe MFA initialization is to capture the nonlinear shape ofthe output manifold Estimating the dynamics is difficult (since the hidden ... made at the beginning ofthe training procedure The first is to judiciously select the placement ofthe RBF kernels in the representation ofthe 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 ofthe EM learning procedure is the...
... divergence ofthe 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 ofthe EKF The state ... augmented with the noise RVs This reduces the dimension ofthe sigma points as well as the total number of sigma points used The covariances ofthe noise source are then incorporated into the state ... in Table 7.3 The complexity ofthe algorithm is of order L3, where L is the dimension ofthe state This is the same complexity as the EKF The most costly operation is in forming the sample prior...
... Chapter 1, all the other chapters present illustrative applications ofthe learning algorithms described here, some of which involve the use of simulated as well as real-life data Much ofthe 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 ofthe system Chapter studies ... for a refined estimation ofthe state xi xii PREFACE Chapter studies yet another novel idea – the unscented Kalman filter – the performance of which is superior to that ofthe extended Kalman filter...
... 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 ofthe 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 ofthe new command button btnNew This code first sets the Boolean variable called mbAddNew to True It then uses the AddNew method ofthe form's...