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2 results on '"GORISSEN, DIRK"'

1. An alternative approach to avoid overfitting for surrogate models.

Author

Nguyen, Huu Minh, Couckuyt, Ivo, Knockaert, Luc, Dhaene, Tom, Gorissen, Dirk, and Saeys, Yvan

Abstract

Surrogate models are data-driven models used to accurately mimic the complex behavior of a system. They are often used to approximate computationally expensive simulation code in order to speed up the exploration of design spaces. A crucial step in the building of surrogate models is finding a good set of hyperparameters, which determine the behavior of the model. This is especially important when dealing with sparse data, as the models are in that case more prone to overfitting. Cross-validation is often used to optimize the hyperparameters of surrogate models, however it is computationally expensive and can still lead to overfitting or other erratic model behavior. This paper introduces a new auxiliary measure for the optimization of the hyperparameters of surrogate models which, when used in conjunction with a cheap accuracy measure, is fast and effective at avoiding unexplained model behavior. [ABSTRACT FROM PUBLISHER]

Published
2011
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2. Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices.

Author

Gorissen, Dirk, Zhang, Lei, Zhang, Qi-Jun, and Dhaene, Tom

Subjects
*MICROWAVE devices, *NONLINEAR statistical models, *INTEGRATED circuits, *LOGIC circuits, *ARTIFICIAL neural networks, *GENETIC algorithms, *EVOLUTIONARY computation
Abstract

This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal–semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function. [ABSTRACT FROM AUTHOR]

Published
2011
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