For 48 years, Stanley Lee has developed innovative ways to use operations research to solve various engineering and social problems. As an established researcher for Kansas State University, he has pioneered an array of projects ranging from alternative energy to water resource management. His techniques have been adopted and implemented by industry leaders across the globe.
DEVELOPING EXPERT SYSTEMS FOR INDUSTRIAL APPLICATIONS
One of Lee's current projects focuses on the representation of complex manufacturing jobs. For instance, an expert responsible for the control of the kiln for manufacturing of cement. While the expert can perform this complicated job, they cannot concisely describe the procedure.This results, at best, in vague or very approximate information.
Lee's research aims to use modern computers to represent human language naturally. He does this through the application of various soft computing techniques such as fuzzy logic and the fuzzy set theory. To overcome the approximation of the description, he uses newly developed learning techniques such as neural network, support vector machines and various evolutionary approaches to update the model as data becomes available.
As a result, Lee has developed various algorithms by fusing soft computing with fuzzy logic/set theory to create the fuzzy adaptive network (FAN); modeled here.
RESEARCH OBJECTIVE - Use computers to find algorithms that model human procedures performed by experts for delicate or very complex jobs.
1. What soft-computing-learning algorithms will best represent available data?
2. Can this technique be used to simplify the hardware applications needed for current manufacturing systems?
3. Can the use of fuzzy sets improve the representation of linguistic models?
ADDITIONAL RESEARCH INTERESTS
- Operations research
- Optimization theory
- Queuing theory
- Intelligent and soft computing
- Uncertainty reasoning
- Support vector machines and neural-fuzzy computing
- Fuzzy logic
- Probabilistic approaches
- Evidence theory