The Siemens Community Catalyst program was co-created with our community to acknowledge technology leaders who consistently contribute to the Siemens Community. Nominations are accepted on a rolling basis.
<operator name="Root" class="Process" expanded="yes"> <description text="This experiment is very similar to the previous experiment. Again, a cross validation building block is used as fitness evaluation. In this case, a genetic algortihm for feature selection is used. This experiment demonstrates two other features of the feature operators of RapidMiner: the stop button which allows the abort of the experiment if the user was already satisfied and the ProcessLog operator which allows online plotting of current fitness values. Please refer to the visualisation sample experiments and the RapidMiner operator reference for further details."/> <parameter key="random_seed" value="1976"/> <operator name="ExampleSetGenerator" class="ExampleSetGenerator"> <parameter key="attributes_lower_bound" value="0.0"/> <parameter key="number_examples" value="200"/> <parameter key="number_of_attributes" value="4"/> <parameter key="target_function" value="sum"/> </operator> <operator name="NoiseGenerator" class="NoiseGenerator"> <parameter key="label_noise" value="0.0010"/> <list key="noise"> </list> <parameter key="random_attributes" value="8"/> </operator> <operator name="GeneticAlgorithm" class="GeneticAlgorithm" expanded="yes"> <parameter key="maximum_number_of_generations" value="12"/> <parameter key="population_size" value="6"/> <parameter key="selection_scheme" value="Boltzmann"/> <parameter key="show_stop_dialog" value="true"/> <operator name="OperatorChain" class="OperatorChain" expanded="yes"> <operator name="XValidation" class="XValidation" expanded="yes"> <parameter key="sampling_type" value="shuffled sampling"/> <operator name="LinearRegression" class="LinearRegression"> <parameter key="feature_selection" value="none"/> </operator> <operator name="OperatorChain (2)" class="OperatorChain" expanded="yes"> <operator name="ModelApplier" class="ModelApplier"> <list key="application_parameters"> </list> </operator> <operator name="RegressionPerformance" class="RegressionPerformance"> <parameter key="main_criterion" value="root_relative_squared_error"/> <parameter key="root_mean_squared_error" value="true"/> <parameter key="root_relative_squared_error" value="true"/> </operator> </operator> </operator> <operator name="ProcessLog" class="ProcessLog"> <list key="log"> <parameter key="gen" value="operator.GeneticAlgorithm.value.generation"/> <parameter key="perf" value="operator.GeneticAlgorithm.value.performance"/> <parameter key="best" value="operator.GeneticAlgorithm.value.best"/> </list> </operator> </operator> </operator></operator>
Join us for a community conversation on how High-Performance Computing (HPC) and Simcenter PSIM work together to meet the increasing demands of modern power electronics simulation. During this one-hour technical session, you'll discover how leveraging HPC can speed up simulations, improve scalability, and empower engineers…
Join us for a community conversation on accelerating your motor simulation workflow with Simcenter Simlab. In this beginner-friendly session, we'll introduce the platform's automated model-building capabilities and show how easy it is to set up your first electric motor analysis. You'll see the basic steps of geometry…
Join us for a Community Conversation where we'll discuss practical approaches to analyzing complex electromagnetic models using hybrid solver techniques. This session will provide a general overview of how different computational methods can be combined within a single simulation to address models containing varied…