Click the constraint item in the explorer to edit its options in the property window. The name, unit and comment can be changed here. As alternative, the constraints editor can be used for a large number of constraints.
Lower Boundary
Lower boundary is the most small value of the constraint which can be accepted by the optimization process. The system reliability of the system is given, if the value of the constraint is greater then the lower boundary:
Constraint ≥ Lower Boundary
If there is no lower boundary for this constraint, set the constraint to a extreme value (e.g. -1E100), which can be never reached by the optimization.
Upper Boundary
Upper boundary is the largest value of constraint, which can be accepted by a optimization process. The system reliability of product is given, if the value of the constraint is smaller then upper boundary:
Constraint ≤ Upper Boundary
If there is no upper boundary for the constraint, set the constraint to a extreme value (e.g. 1E100), which can be never reached by an optimization.
Weight
The violation of constraint boundaries (upper and lower) will be treated with a penalty function, which is balanced by the weight between the defined constraints. The total penalty is the weighted sum of all single penalties With the option Weight, the total penalty function can be influenced directly. The option is normally set to 1.
Cluster
Cluster Method
The used method for clustering: "Non-Cluster", "Userdefined", "Binary Tree", "K-Means" and "HDBSCAN*". "Userdefined" does nothing and it is a manuall clustering method, where user should select data points from DOE-Table or Scatter-Plot and add to new cluster manually.
Cluster Size
The max. number of data point for each cluster.
Cluster Type
The given cluster size can be minimal or maximal for all clusters.
Normed Space
If it is True, all data will be standardized for a normed space. In this case, alternating dimension will be chosen for axis spliting by Binary Tree. If it is False, the widest dimension will be chosen for axis spliting.
Axis Spliting
User can choose the point as Center or Mean Value for the spiting the axis.
Cluster Number
This is the number of all clusters
Neighbor Size
This is the number of neares neighbors
Random Initialization
The random generator for can be initialized by "Time Dependent Seed" or "User Defined Seed". By "Time Dependent Seed", the random seed will be generated by the actual time and thus, the random numbers and its clustering results will be different by re-starting clustering. The last used random seed will be saved in the option "Random Seed". By "User Defined Seed", the seed can be set manually.
Random Seed
This is the seed for initialization of the random number generator.
Include All Parameters
If it is True, all parameters will be include for clustering. If the option is False, a list of all parameters will be shown and user can select any parameter for clustering
Include Criteria
If it is True, user can select a list of criteria as additional parameter for clustering
Include Constraints
If it is True, user can select a list of constraints as additional parameter for clustering
Approximation
The method of approximation can be setup individually. There are 2 possible methods Polynomial and Gaussian Process to choose. It is normally set to "Auto-Approximation". In this case, the best approximation method from polynomial or Gaussian process will be chosen automatically for this variable. This setting is not valid for the moment methods, because these methods use first or second order Taylor series for the approximation
Include All Parameters
If the option is selected, all stochastic parameters of the experiment will be input parameter for the approximation (metamodel). If it is not selected, user can choose any stochastic parameters as input parameter from all stochastic parameters of the experiment for the approximation.
Gaussian Process
Covariance Function
If the approximation is set to "Gaussian Process", this option will be available. They are different implemented covariance functions for the Gaussian process being selected: "Best Covariance", "Square Exponential", "Exponential", "Gamma-Exponential", "Matern Class 3/2", "Matern Class 5/2", "Rational Quadratic" and "Periodic". The covariance function represents the interpolation between support points in the design space. It is an assumption and a critical factor of the Gaussian process. The option is normally set to "Best Covariance". In this case, the all availble covariances will be calculated to choose the best covariance for this variable.
Low-Rank Approximation
There are 3 options "Full Matrix", "Low-Rank Matrix" and "Hierarchical Matrix". The option "Full Matrix" will use full input data for the Gaussian process. By "Low-Rank Matrix", the low-rank approximation for the Gaussian process will be carried out for reducing number of input data points.
Approximation Rank [%]
This option allows users to input the exact rank of the input matrix by low-rank approximation. This rank is caculated by percentage of the full input matrix size. If the value is 100, the full input matrix will be used for Gaussian process. If the value is 50, the half of the input matrix will be taken. If the both options "Approximation Rank" and "Approximation Error" are given, the used low-rank of the input matrix will be the min. rank of both calculation cases.
Approximation Error [%]
This option allows users to input the exact error of the low-rank approximation. This error is caculated by percentage of the maximal eigenvalue of the input matrix. All eigenvalues of the matrix will be cut off, if they are smaller then this approximation error. If the value is zero, all ranks of the input matrix is used for least square. If the value is 100, only 1 rank is used. If the both options "Approximation Rank" and "Approximation Error" are given, the used low-rank of the input matrix will be the min. rank of both calculation cases.
Gaussian Noise [%]
This option is only visible if the option "Noise Optimization" = False for design of experiment. The value is defined as procentage of the absolute difference |Ymax-Ymin| of the cosntraint. The Gaussian noise is very important for the meta-model. It decides about the smoothness, accuracy and existence of the meta-model. If the Gaussin noise is big, the model is very smooth, but the accuracy is bad. If the Gaussian noise is smaller, the smoothness of the meta-model is worse, but the auccuracy is better. Depending on the concrete data, if the Gaussian noise is too small, the solution of the Gaussian process cannot be found. All parameters and covariances are zero. If the noise optimzation for design of experiment is turn-on, the optimal Gaussian noise will be found automatically.
Polynom
Polynomial Type
This is the order type of the polynomial regression for the approximation. There are 3 options to choose "Best Order", "Uniform Order" and "Manual Order". If "Uniform Order" is selected, user can set the same polynomial order for all parameters. Otherwise, different polynomial orders can be set for single parameters if "Manual Order" is selected. By "Best Order", the best polynomial order for different parameters will be calculated autmatically.
Polynomial Order
User can set here the order for the uniform polynomial regression.All parameters will have got the same polynomial order.
Low-Rank Approximation
There are 2 available options: "Full Matrix" and "Low-Rank Matrix". By "Low-Rank Matrix", the low-rank approximation will be ued for regularization of the least square method.Other option takes the full matrix for calculation of least square.
Approximation Error [%]
This option allows users to reduce the rank of input matrix at the low-rank approximation. This apprximation error is caculated by percentage of the maximal eigenvalue of the input matrix. All eigenvalues of the matrix will be cut off, if they are smaller then this approximation error. If the value is zero, all ranks of the input matrix is used for least square. If the value is 100, only 1 rank is used.
Acceptable Failure
It is possible to define a acceptable failure in range [0..1] for the constraint. If a failure arises and it is smaller then the acceptable failure, the solution can be also considered as reliable. The acceptable failure is only visible for moment methods.