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2000. Chicago, USA. 4). 2001. OMG: Requirements for UML Profile. 1999. : Heavyweight extensions to the UML metamodel to describe the C3 architectural style. ACM SIGSOFT Notes, 2003. 28(3). 4. 2002. : Metamodelling for Model-Driven Development (draft): To be published. 2005. com) Stiell IG, McKnight RD, Greenberg GH, McDowell I, Nair RC, Wells GA, et al. Implementation of the Ottawa ankle rules. JAMA 1994;271:827-32. cn Abstract: This paper presents dynamic-fuzzy logic (DFL) and its usefulness in cognitive/affective modeling.
The main attribute of the adaptive controller is its ability to accommodate system changes. adaptive fuzzy decoupling controller by changing the engine flight environment. 5 and 18km altitude. It is observed the system exhibits good tracking with the reference model even after engine flight environment changes significantly. 4 CONCLUDING REMARKS The key advantages of the adaptive fuzzy decoupling controller are: 1. Providing an automatic method to synthesize a portion of the knowledge-base for the direct fuzzy controller while at the same time it ensures that the system will behave in a desirable fashion 2.
4 +0. 6 +0. 8 +1. 0 +1. oi +21-0. 6 -0. 4 -0. 2 0. 0 +0. 2 +0. 4 +0. 6 +0. 8 +1. 0 +1. 0 +1. ()i +31-0. -0. 1 ^+0. 1 i+O. 2 +0. 4 +0. 6 +0. 8 +1. 0 +1. 0 +1. 0 +1. Oi :+4K). 21+0. I i+O. 3|+0. 4+0. 6+0. 8 + 1. 0 + 1. 0 + 1. 0 + 1. 0 + 1. oi I :+5| 0. 0+0. 2+0. 4+0. 6+0. 8 + 1. 0 + 1. 0+1. 0 + 1. 0 + 1. 0 + 1. oj Figure 2. 3 The Learning Mechanism As previously mentioned, the learning mechanism performs the function of modifying the knowledgebase of a direct fuzzy controller so that the closed-loop system behaves like the reference model.