Linearizing the Characteristics of Gas Sensors using Neural Network

The paper describes implementing arbitrary connected neural network with more powerful network architecture to be embedded in inexpensive microcontroller. Our objective is to extend linear region of operation of nonlinear sensors. In order to implement more powerful neural network architectures on microcontrollers, the special Neuron by Neuron computing routine was developed in assembly language to allow fastest and shortest code. Embedded neural network requires hyperbolic tangent with great precision was used as a neuron activation function. Implementing neural network in microcontroller makes superior to other systems in faster response, smaller errors, and smoother surfaces. But its efficient implementation on microcontroller with simplified arithmetic was another challenge. This process was then demonstrated on gas sensor problem as they were mainly used accurately in measuring gas leakage in industry.