Reinforcement learning neural network circuits for electronic nose

This paper approaches electronic nose design from two promising angles: reinforcement neural network (RNN) learning algorithms, and naturally compatible analog circuits. This approach is inspired by biological sensing and discrimination of a multitude of odors in a background environment. A VLSI system approach is presented for classification of chemical compounds, with knowledge of key features only. Based on utilizing microsensor arrays, reinforcement neural networks are used to affect nonparametric pattern recognition, classification, and distinction among multicomponent chemicals. A specialized RNN approach is chosen. Realization and implementation of analog RNN circuits is presented using 1.2 μm CMOS n-well technology, at AMI, through the MOSIS facilities. Preliminary results are satisfactory and lend evidence to the effectiveness of the analog designed neural network building blocks for temporal and spatial NN pattern recognition