Multi-Sensor Integration System utilizing Fuzzy Inference and Neural Network
Koji Shimojima, Toshio Fukuda, Fumihito Arai and Hideo Matsuura
Faculty of Mechanical Engineering of Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-01, Japan
Received:June 4, 1992Accepted:July 17, 1992Published:October 20, 1992
Keywords:Robotics, Measurement, Sensor integration system, Fuzzy set theory, Neural network, Knowledge data base of sensors, Inferred error
Sensors are important for recognizing the system state environmental status in the intelligent robotic system. Thus, the sensor integration system (SIS) has been studied in a wide range of applications. In this paper, it is shown that the SIS can expand the measurable region of sensors with higher accuracy by multiple sensors and that operators can use the system as easily as a single high-performance sensor system. Systems which have been reported so far do not have flexibility for changing/replacing sensors. Thus, this paper presents an approach to the SIS with the knowledge data base of sensors, so the proposed SIS has the flexibility for changing/replacing sensors. This system consists of four subsystems: 1) sensors as hardware sensing devices, 2) knowledge data base of sensors (KBS), 3) fuzzy inference, and 4) neural network(NN). This system can estimate the error for the sensor’s measured value by fuzzy inference with KBS. The measured values are integrated by NN. The inferred error and measured value are put into NN. Then, NN’s output gives the integrated value of multiple sensors. The proposed system is shown to be effective through extensive experiments.
Cite this article as:K. Shimojima, T. Fukuda, F. Arai, and H. Matsuura, “Multi-Sensor Integration System utilizing Fuzzy Inference and Neural Network,” J. Robot. Mechatron., Vol.4 No.5, pp. 416-421, 1992.Data files: