A neural network model for adaptive, non-uniform A/D conversion
Marc M. Van Hulle · 2002
An adaptive feedforward network is presented for performing non-uniform, flash-type analog-to-digital (A/D) conversion. The unsupervised competitive learning rule used, called boundary adaptation rule (BAR), maximizes entropy and provides an efficient nonuniform quantization of the analog signal range. The network is easily implementable in VLSI circuitry and meets the requirements of smart sensors. It is shown that the network is able to adapt itself to rapidly changing input signals, such as speech signals.>