Development of intelligent sensors using Legendre functional-link artificial neural networks

Different types of sensors are used to control and monitor complex systems in many applications, where the environmental parameters, e.g., temperature, humidity, etc., undergo large variations. In such conditions, the sensor's output may be erroneous and the system being controlled may malfunction. The need of intelligent sensors arise in such situations. These sensors should be capable of compensating for the adverse effects of the environmental conditions on the sensor output and linearization of sensor response, in order to provide correct readout. In this paper, we propose a novel computationally efficient Legendre functional-link artificial neural network (L-FLANN) to develop a smart sensor that can compensate for the adverse effects of the environmental conditions. By taking two types of environmental models and a pressure sensor, we have shown with extensive computer simulations that the proposed smart sensor is computationally efficient with respect to a multi-layer perceptron (MLP)-based sensor model and capable of satisfactory linearization of sensor output. This smart sensor produces only plusmn0.5% full-scale error between the actual and estimated output for the two selected environmental models under a temperature variation of -50 to 200degC.