Approaches to realize high precision analog-to-digital converter based on wavelet neural network

Abstract

A new method is proposed in this paper to implement the high precision analog-to-digital converter (ADC) with low precision ADC based on two-stage conversion. Because the main error of ADC is non-linear, an algorithm using wavelet neural network for compensating error and non-linearity of ADC is proposed, which has faster speed quality convergence and higher precision than BP neural network. By studying the theories and scope of ADC errors, the wavelet neural network is used to deal with the non-linearity part of ADC error, which simplifies the network structure and requires shorter training and less iterations of learning. The experimental results show that with the wavelet approximation, the non-linearity of ADC can be reduced markedly, and the conversion speed of ADC can maintain maximum.