Abstract
The identification of metabolic systems such as metabolic pathways, enzyme actions, and gene regulations is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE's have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm (GA) to identify uncertain parameters in the model.
| Original language | English (US) |
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| Title of host publication | IEEE International Conference on Fuzzy Systems |
| Publisher | IEEE |
| Pages | 220-225 |
| Number of pages | 6 |
| Volume | 1 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3) - New Orleans, LA, USA Duration: Sep 8 1996 → Sep 11 1996 |
Other
| Other | Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3) |
|---|---|
| City | New Orleans, LA, USA |
| Period | 9/8/96 → 9/11/96 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics