Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment | EURASIP Journal on Applied Signal Processing 2005 4 558-574 2005 Hindawi Publishing Corporation Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment Jagdish C. Patra Division of Computer Communications School of Computer Engineering Nanyang Technological University Singapore 639798 Email aspatra@ Ee Luang Ang Division of Computer Communications School of Computer Engineering Nanyang Technological University Singapore 639798 Email aselang@ Narendra S. Chaudhari Division of Information Systems School of Computer Engineering Nanyang Technological University Singapore 639798 Email asnarendra@ Amitabha Das Division of Computer Communications School of Computer Engineering Nanyang Technological University Singapore 639798 Email asadas@ Received 11 February 2004 Revised 5 July 2004 Recommended for Publication by John Sorensen We present an artificial neural-network- NN- based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters with high accuracy. To show the potential of the proposed NN-based framework we provide results of a smart capacitive pressure sensor CPS operating in a wide temperature range of 0 to 250 C. Through simulated experiments we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale FS error of only over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- MCU- based implementation scheme is also provided. Keywords and phrases intelligent sensors artificial neural networks autocompensation. 1. INTRODUCTION In many practical .
