Soft Sensing

Soft Sensing - Soft sensing can be thought of as a regression tool whose main task is to synthesize static/dynamic linear/nonlinear models from plant data. Here it is assumed that the inputs are potentially cross-correlated and quite possibly degenerate. As such a host of tools are provided to obtain practical models under these conditions. A variety of different models are supported and the intent is to add new model types and analysis tools as appropriate. In addition to dealing explicitly with correlated inputs, the regression tools provide both statistics and metrics to provide a meaningful interpretation of model quality. This information can be used to compare and contrast different model and input types.

 

Principal component analysis 

used to select dimension of the input sub-space

Models - 

1. OLS – Ordinary Least Squares. Conventional least squares models.
2. WLS – Weighted Least Squares or more commonly called Robust Regression. These models are desensitized to outliers in the data. While the robust regression models require a nonlinear solution, the final model is in fact linear.
3. PLS – Partial Least Squares. PLS models can be either linear or nonlinear depending on the selection of the subspace regression. A polynomial model is assumed for the subspace regression. Its order is user selectable.
4. DSS – Dynamic Sub-Space. If dynamics are desirable in the inferential calculation, then DSS models can be used. Model structure, delay and order are determined automatically. All models are ranked and displayed in LaPlace domain form. Since a sub-space solution is used, the user specifies the desired level of captured variance in the solution. The dimension of the sub-space is then automatically determined using an SVD factorization.
5. UES – Nonlinear User Entered System of Equations. The UES model provides a solution to a nonlinear regression problem with constraints. The user can define the nonlinear relationship and the input variables of interest.