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AutoQuiz: How Do You Model Linear Relationships for a Large Number of Correlated Inputs?

AutoQuiz is edited by Joel Don, ISA's social media community manager.

Today's automation industry quiz question comes from the ISA Certified Automation Professional certification program.  ISA CAP certification provides a non-biased, third-party, objective assessment and confirmation of an automation professional's skills. TheAutoQuiz-CAP-modeling-linear-relationships CAP exam is focused on direction, definition, design, development/application, deployment, documentation, and support of systems, software, and equipment used in control systems, manufacturing information systems, systems integration, and operational consulting. Click this link for information about the CAP program. This question is from the CAP study guide, Domain II.

What is the MOST appropriate technique for modeling linear relationships for a large number of correlated inputs where the equations are unknown?

a) artificial neural networks
b) multivariable statistical process controls
c) step response models
d) first principle models
e) none of the above


Artificial neural networks (ANN) excel at modeling nonlinear relationships for a relatively large unknown number of inputs. However, the inputs don't correlate, and the training data must cover the whole region. An ANN cannot extrapolate values outside the test region and doesn't handle large lags well. Multivariable statistical process control excels at modeling unknown linear relationships for a large number of inputs that correlate.

Step response models excel at linear relationships for a small to moderate number of uncorrelated inputs where dynamics are important. Step response models work for linear dynamic on-line property estimates. First principle models require known equations and parameters that use process principles and material and energy balances.

The correct answer is B, multivariable statistical process controls.


Joel Don
Joel Don
Joel Don is the community manager for ISA and is an independent content marketing, social media and public relations consultant. Prior to his work in marketing and PR, Joel served as an editor for regional newspapers and national magazines throughout the U.S. He earned a master's degree from the Medill School at Northwestern University with a focus on science, engineering and biomedical marketing communications, and a bachelor of science degree from UC San Diego.

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