Advanced Modeling In Biological Engineering Using Soft Computing Methods
- Conference
- Location
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Honolulu, Hawaii
- Publication Date
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June 24, 2007
- Start Date
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June 24, 2007
- End Date
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June 27, 2007
- ISSN
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2153-5965
- Conference Session
- Tagged Division
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Biological & Agricultural
- Page Count
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16
- Page Numbers
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12.184.1 - 12.184.16
- DOI
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10.18260/1-2--2929
- Permanent URL
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https://peer.asee.org/2929
- Download Count
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430
Paper Authors
George Meyer University of Nebraska-Lincoln
GEORGE MEYER, Professor, has taught graduate and undergraduate classes that involve plant and animal growth and environmental factors, modeling, and instrumentation and controls for both agricultural and biological systems engineering students for 28 years. He has received national paper awards and recognition for his work in distance education and has received university teaching awards. His current research include measurement and modeling of crop water stress, fuzzy logic controls for turf irrigation management, and machine vision detection, enumeration, and plant species identification for spot spraying control and precision agriculture.
David Jones University of Nebraska-Lincoln
DAVID JONES, Professor, has taught graduate and undergraduate classes that involve fuzzy set theory and soft computing techniques, risk assessment of complex systems, and mathematical modeling of physical and biological systems for the past 18 years. He also teaches a Heat and Mass Transfer course to engineering juniors and the senior design classes. He has received numerous university and national awards for his teaching excellence.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Advanced Modeling in Biological Engineering Using Soft-computing Methods Abstract
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.
Keywords: Courseware, biological systems, modeling, fuzzy systems, optimization
Meyer, G., & Jones, D. (2007, June), Advanced Modeling In Biological Engineering Using Soft Computing Methods Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. 10.18260/1-2--2929
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