Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selects the relevant characteristics until the complete classification is performed. The basic incremental synthesis method, including the supervised training procedure, is derived to design application-tailored neural paradigms with good generalization capability.
Entropic analysis and incremental synthesis of multilayered feedforward neural networks / A. Pelagotti, V. Piuri. - In: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS. - ISSN 0129-0657. - 8:5-6(1997), pp. 647-659. [10.1142/S0129065797000574]
Entropic analysis and incremental synthesis of multilayered feedforward neural networks
V. PiuriUltimo
1997
Abstract
Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selects the relevant characteristics until the complete classification is performed. The basic incremental synthesis method, including the supervised training procedure, is derived to design application-tailored neural paradigms with good generalization capability.Pubblicazioni consigliate
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