A Comparison of the Neural Platform Across JMP 7.0, 8.0, and 9.0
Patrick Biltgen, PhD, Senior Principal Systems Engineer – BAE Systems
The JMP Neural platform has evolved significantly since its introduction in JMP 6 and is used heavily in the aerospace and defense simulation community to approximate high-fidelity complex systems simulations. This paper will review and compare neural network models across common data sets created in the last three versions of JMP and comment on the improvements and advancements of this tool set for complex systems designers. By comparing goodness of fit statistics, ease of creation and flexibility to address multiple complex models, the author will evaluate the utility and flexibility of the latest neural platform and demonstrate how JMP 9 can be used to analyze and visualize the results of complex simulations. Examples will include large agent-based simulations and models with mixed continuous and discrete variables. The presentation will also demonstrate the use of the Prediction Profiler with multiple neural equations and the use of the Simulator to rapidly flood a large design space with many points to enable “inverse design” for capability-based analysis.