Title :
A neural network model for adaptive, non-uniform A/D conversion
Author :
Hulle, Marc M Van
Author_Institution :
Dept. of Brain & Cognitive Sci., MIT, Cambridge, MA, USA
Abstract :
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
Keywords :
analogue-digital conversion; feedforward neural nets; maximum entropy methods; signal processing; unsupervised learning; adaptive feedforward network; boundary adaptation rule; maximum entropy; neural network model; nonuniform A/D conversion; nonuniform quantization; smart sensors; speech signals; unsupervised competitive learning; Adaptive systems; Biological neural networks; Circuits; Entropy; Intelligent sensors; Neural networks; Neurons; Quantization; Sensor phenomena and characterization; Very large scale integration;
Conference_Titel :
Neural Networks for Processing [1993] III. Proceedings of the 1993 IEEE-SP Workshop
Conference_Location :
Linthicum Heights, MD
Print_ISBN :
0-7803-0928-6
DOI :
10.1109/NNSP.1993.471832
