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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS--I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 49, NO. 12, DECEMBER 2002 1799 Selecting Accurate, Robust, and Minimal
 

Summary: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS--I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 49, NO. 12, DECEMBER 2002 1799
Selecting Accurate, Robust, and Minimal
Feedforward Neural Networks
Cesare Alippi, Senior Member, IEEE
Abstract--Accuracy, robustness, and minimality are fun-
damental issues in system-level design. Such properties are
generally associated with constraints limiting the feasible model
space. The paper focuses on the optimal selection of feedforward
neural networks under the accuracy, robustness, and minimality
constraints. Model selection, with respect to accuracy, can be
carried out within the theoretical framework delineated by the
final prediction error (FPE), generalization error estimate (GEN),
general predicion error (GPE) and network information criterion
(NIC) or cross-validation-based techniques. Robustness is an
appealing feature since a robust application provides a graceful
degradation in performance once affected by perturbations in
its structural parameters (e.g., associated with faults or finite
precision representations). Minimality is related to model selection
and attempts to reduce the computational load of the solution
(with also silicon area and power consumption reduction in a

  

Source: Alippi, Cesare - Dipartimento di Elettronica e Informazione, Politecnico di Milano

 

Collections: Computer Technologies and Information Sciences