多异构输出神经网络的剪枝方法

F. Grasso, A. Luchetta, S. Manetti
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引用次数: 0

摘要

提出了一种新的多输入多输出前馈人工神经网络剪枝阈值选择的完整方法。它是基于对网络的任何单个输出计算的局部灵敏度指数的评价。特别强调了一类具有多个异构输出的神经网络。它将展示如何通过从数据的非线性相关性中推导出ldquoimportance指数来考虑输出的非齐次性质。本文将通过开发一种专门用于特定多输出反演系统的神经结构来展示所提出方法的一个示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A pruning method for multiple heterogeneous output neural networks
A new complete procedure for the selection of pruning threshold in MIMO (multiple input multiple output) feedforward artificial neural networks (FANN) is presented. It is based on the evaluation of a local sensitivity index calculated with respect of any single output of the network. Special emphasis is given to a particular class of neural networks with multiple heterogeneous outputs. It will be shown how to take into account of the non-homogeneous nature of the outputs by deriving an ldquoimportance indexrdquo from the nonlinear correlation of data. An example of the proposed method will be shown by the development of a neural architecture devoted to a specific multi-output inversion system.
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