Neural net based torque sensor using birefringent materials

Sensors and Actuators, A: Physical

Volumn 70, Issue 3, 1998, Pages 243-249

  Chung, Dukki ^a   Merat, Francis L ^a   Discenzo, Fred M ^b   Harris, James S ^b  

^a Case Western Reserve University   (United States)

^b ROCKWELL AUTOMATION   (United States)

Author keywords

Photoelasticity; Strain; Torque

Indexed keywords

BIREFRINGENCE; CHARGE COUPLED DEVICES; IMAGE PROCESSING; NEURAL NETWORKS; PHOTOELASTICITY; SHAFTS (MACHINE COMPONENTS); STRAIN GAGES; STRESS ANALYSIS; TORQUE MEASUREMENT;

TORQUE SENSORS;

OPTICAL SENSORS;

EID: 0032188647     PISSN: 09244247     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0924-4247(98)00147-2     Document Type: Article

References (7)

1. A.S. Kobayashi, Handbook on Experimental Mechanics, 2nd rev. edn., VCH Publishers, 1993.
2. Introduction to Stress Analysis by the Photo Stress Method, Tech. Note TN-702-1, Measurements Group, 1989.
3. K. Hornik, M. Stinchombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (1989) 359-369.
4. K. Funahashi, On the approximation realization of continuous mappings by neural networks, Neural Networks 2 (1989) 183-192.
5. W.S. Newman, Comparative Evaluation of Three Transmission Types: Worm Gear, Cone Drive and Traction Drive, CAISR Technical Report 93-109, Center for Automation and Intelligent Systems Research, Case Western Reserve University, 1993.
6. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Chap. 8, MIT Press, 1986.
7. S.E. Fahlman, Faster learning variations on back propagation: an empirical study, Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann Publishers, San Mateo, CA, 1988.