Compensation of Sensors Nonlinearity with Neural Networks

This paper describes a method of linearizing the nonlinear characteristics of many sensors using an embedded neural network. The proposed method allows for complex neural networks with very powerful architectures to be embedded on a very inexpensive 8-bit microcontroller. In order to accomplish this unique training software was developed as well as a cross compiler. The Neuron by Neuron process was as developed in assembly language to allow the fastest and shortest code on the embedded system. The embedded neural network also required an accurate approximation for hyperbolic tangent to be used as the neuron activation function. This process was then demonstrated on a robotic arm kinematics problem.