Reinforcement neural learning with application to gas sensors

Neural net reinforcement learning algorithm and its application to gas (chemicals) classification and quantification are considered in this paper. The approach does not require memory and has simplified reward/punishment computations. The advantages and disadvantages of the reinforcement algorithm are contrasted against other competing neural algorithms. The reinforcement learning has been found practical, when each gas (chemical) is distinguished by several features and characteristics with possible overlap. The paper also gives an account of integrated circuit digital chip implementation for typical four gases with temperature, pressure, and flow quantity features. The number of required iterations has been found to depend on reward and penalty parameters, as well as the threshold governing the learning. It is believed that our implementation approach has potential uses in auto industry safety and emission control, as well in automated semiconductor manufacturing process.