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Sensitivity-Based Adaptive Learning Rules for Binary Feedforward Neural Networks

机译：二进制前馈神经网络的基于灵敏度的自适应学习规则

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摘要

This paper proposes a set of adaptive learning rules for binary feedforward neural networks (BFNNs) by means of the sensitivity measure that is established to investigate the effect of a BFNN's weight variation on its output. The rules are based on three basic adaptive learning principles: the benefit principle, the minimal disturbance principle, and the burden-sharing principle. In order to follow the benefit principle and the minimal disturbance principle, a neuron selection rule and a weight adaptation rule are developed. Besides, a learning control rule is developed to follow the burden-sharing principle. The advantage of the rules is that they can effectively guide the BFNN's learning to conduct constructive adaptations and avoid destructive ones. With these rules, a sensitivity-based adaptive learning (SBALR) algorithm for BFNNs is presented. Experimental results on a number of benchmark data demonstrate that the SBALR algorithm has better learning performance than the Madaline rule II and backpropagation algorithms.
机译：本文提出了一套针对二值前馈神经网络（BFNN）的自适应学习规则，该算法是通过研究BFNN的权重变化对其输出的影响而建立的灵敏度度量。这些规则基于三个基本的适应性学习原则：利益原则，最小干扰原则和负担分担原则。为了遵循利益原则和最小干扰原则，开发了神经元选择规则和权重适应规则。此外，制定了学习控制规则以遵循负担分担原则。规则的优势在于它们可以有效地指导BFNN的学习，以进行建设性的适应并避免破坏性的适应。利用这些规则，提出了一种基于灵敏度的BFNNs自适应学习（SBALR）算法。在大量基准数据上的实验结果表明，SBALR算法比Madaline规则II和反向传播算法具有更好的学习性能。

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