Design of Experiment, Mathematical Modeling, and Optimization
Melih Savran, Levent Aydın · 2024
This chapter provides an introduction to experimental design methods, mathematical modeling methods, and stochastic optimization methods. The chapter discusses the necessary conditions that must be met for the correct and effective use of the most commonly known experimental design methods, including the Full Factorial, Box-Behnken, Central Composite, D-Optimal, and Taguchi methods. Additionally, this chapter presents original mathematical modeling methods, namely Neuro Regression and Stochastic Neuro Regression, which have the potential to be an alternative to the most popular methods such as ANN and RSM. It also provides a detailed explanation of how optimization is carried out using the Mathematica program and its optimization methods. Finally, the efficacy of these methods is demonstrated by using test functions found in the literature.