Advanced Modeling in Biological Engineering Using Soft-computing Methods
David D. Jones, George E. Meyer · 2008 Providence, Rhode Island, June 29 - July 2, 2008 · 2008
A engineering graduate course on advanced modeling techniques and applications provides both basic and practical understanding of techniques for simulating biological and environmental processes to future scientists and research engineers A primary course emphasis was the inclusion of fuzzy set theory and the positioning of fuzzy set theory within a broader topic of soft computing. Students developed their own paradigms and semester projects related to their particular research interest. Principle course topics included fuzzy variables, inference systems, neural networks, signal processing, controls, visual simulation, machine vision, and genetic algorithms in support of modeling. Students were expected to read and critique related journal articles each week. To enhance communication skills, students lead selected class sessions by discussing and critiquing refereed articles related to soft computing and modeling, especially within their chosen research areas. This paper discusses the course content and topics presented, and how the course continues to evolve. A summary of student projects and results are also presented.