Selection of weight quantisation accuracy for radial basis function neural network using stochastic sensitivity measure
Selection of weight quantisation accuracy for radial basis function neural network using stochastic sensitivity measure
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DOI：
10.1049/el:20030499
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发表时间：
2003-05
影响因子：
1.1
通讯作者：
Wing W. Y. Ng;D. Yeung
中科院分区：
文献类型：
--
作者：
Wing W. Y. Ng;D. Yeung
在径向基函数神经网络（RBFNN）的硬件实现中，最小化每个连接权重的位数将导致高速和低成本的实现，但可能增加输出误差。提出了一种权重量化精度选择方法，为给定的随机灵敏度度量找到合适的比特数，该度量量化了输出误差方差与输入、权重及其扰动的一阶和二阶统计量之间的关系。
Minimising the number of bits per connection weight in hardware realisation of a radial basis function neural network (RBFNN) will result in high-speed and low-cost implementation, with possible increase in output error. A weight quantisation accuracy selection method is proposed, to find an appropriate number of bits for a given stochastic sensitivity measure, which quantifies the relationship between the variance of the output error and first- and second-order statistics of input, weight and their perturbations.