AC 2007-2729: ADVANCED MODELING IN BIOLOGICAL ENGINEERING USING SOFT-COMPUTING METHODS
George E. Meyer, David Jones · 2007
A new 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. Of particular importance are those models that benefit with soft computing methods. Simulation of biological and environmental systems involves the treatment of vagueness, uncertainty, and incomplete information usually associated with these systems. A primary course emphasis was the inclusion of fuzzy set theory and the positioning of fuzzy set theory (FST) within a broader topic of soft computing. At the conclusion of the course, students had developed their own paradigms and semester projects related to their particular research interest. Students made use of current literature for theory formation and hypothesis building related to biological and environmental systems. Future researchers must effectively use methods to simulate ambiguous systems for directing limited resources toward the solution of these problems. 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. Students learned practical modeling skills using MATLAB® , MATHCAD®, and LabVIEW® programming exercises. 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.