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Selection of weight quantisation accuracy for radial basis function neural network using stochastic sensitivity measure

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2 Author(s)
Ng, W.W.Y. ; Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China ; Yeung, D.S.

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.

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Electronics Letters  (Volume:39 ,  Issue: 10 )