A multi-layer perceptron neural network with floatingpoint number system is implemented on a field programmable gate array (FPGA). IEEE-754 32-bit single precision floatingpoint number is used to represent values in the neural network accurately. The neural network forms the core of an intelligent sensor system which has the ability to mitigate the nonlinear influence on the sensor output by external disturbances. Training is performed on the neural network to approximate the response characteristics of a sensor for different level of disturbances so as to compensate for the nonlinearity. The intelligent sensor system is implemented on Celoxica RC203E development board which contains a Xilinx Virtex-II FPGA chip. A custom-built intelligent light intensity sensor is used for experimentation and the neural network is able to achieve a maximum full-scale (FS) error of plusmn1.5% under the nonlinear influence caused by the varying distance between the sensor and the light source. In terms of root mean squared error (RMSE), it is able to achieve a RMSE of 0.0052
['Jagdish Chandra Patra', 'Han Yang Lee', 'Pramod Kumar Meher', 'Ee-Luang Ang']
Field Programmable Gate Array Implementation of a Neural Network-based Intelligent Sensor System