The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support vector machines for analog circuit performance representation
Proceedings of the 40th annual Design Automation Conference
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Performance Modeling of Analog Integrated Circuits Using Least-Squares Support Vector Machines
Proceedings of the conference on Design, automation and test in Europe - Volume 1
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
A Two-Level Modeling Approach to Analog Circuit Performance Macromodeling
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
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Functional verification by simulation is an important step during the development of present microelectronic solutions for automotive applications. Its relevance is based on the capability to compare the behavior of a developed circuit with its specification. Since the transient simulation of application specific integrated circuits (ASICs) normally shows long runtimes, the behavior of time-critical components is manually modeled in order to speed up simulation. The present article describes a data-based approach for semi-automated generation of behavioral models for analog mixed-signal (A/MS) systems. The approach is based on support vector machines and a transformation dictionary for extraction of dynamic properties. The application of this method results in highly accurate pin-compatible behavioral models for A/MS systems with a significant reduction in simulation times. Additionally, the generated models can be easily integrated in description languages like VDHL-AMS, Verilog-AMS, Simulink and MAST. Another benefit of the proposed method consists in its flexibility to model systems of different physical domains. The emphasized properties will be illustrated by the modeling of two examples belonging to analog-digital and electromechanic systems respectively.