Soft Computing and Fuzzy Logic
IEEE Software
Creating and Using Models for Engineering Design: A Machine-Learning Approach
IEEE Expert: Intelligent Systems and Their Applications
Learning Fuzzy Rule-Based Neural Networks for Control
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Assessment of simulation models based on trace-file analysis: a metamodeling approach
Proceedings of the 30th conference on Winter simulation
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Analysis of simulation models has gained considerable interest in the past. However, their complexity still remains a considerable drawback in practical applications. One promising concept is the building of auxiliary models (metamodels) for different analysis goals. We present an efficient algorithm that constructs a metamodel only from simulation data, so no a priori knowledge has to be included. It will be shown that the resulting system approximates real valued functions with an adjustable precision. In addition the data can contain fuzzy patterns or values with a corresponding confidence-interval. This is especially well suited for simulation data due to its stochastic character. The metamodel is represented in form of a Fuzzy Graph which allows the analyst to directly extract easy to interpret if-then-rules. Application of this method to a real world token bus model is shown in detail.