Efficient Training Algorithm for Neuro-Fuzzy Network and its Application to Nonlinear Sensor Characteristic Linearization

Abstract

An ideal linear sensor is one for which input and output values are always proportional. Typical sensors are, in general, highly nonlinear or seldom sufficiently linear enough to be useful over a wide range or span of interest. Due to the requirement of tedious effort in designing sensor circuits with sufficient linearity for some applications, the word nonlinearity has acquired a pejorative connotation. Hence, a computationally intelligent tool for extending the linear range of an arbitrary sensor is proposed. The linearization technique is carried out by a very efficiently trained neuro-fuzzy hybrid network which compensates for the sensor’s nonlinear characteristic. The training algorithm is very efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than any first order training algorithm. Linearization of a negative temperature coefficient thermistor sensor with an exponentially decaying characteristic function is used as an application example, which demonstrates the efficacy of the procedure. The proposed linearization technique is also applicable for any nonlinear sensor (such as J-type thermocouple or pH sensor), whose output is a monotonically increasing/decreasing function.