By Jesъs Arбmburo and Antonio Ramнrez Treviсo
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Additional info for Advances in Robotics Automation and Control
1361-1366. 32 Advances in Robotics, Automation and Control Sunji Mori, H. Nishida & H. Yamada. (1999). Optical Character Recognition. L. Mitchell. (1993). The JPEG Still Image Data Compression Standard. New York: Van Nostrand Reinhold. F. J. Fernando. (2003). Towards Automatic Transcription of Syriac Handwriting. Int’l Conf. Image Analysis and Processing, pp. 664- 669. 3 Predicting Surface Roughness in Grinding using Neural Networks Paulo R. Aguiar, Carlos E. D. Cruz, Wallace C. F. Paula and Eduardo C.
42 Advances in Robotics, Automation and Control In the second group, errors of about 10-1 µm was thought as bad ones, but it would be mostly fine in industry depending on the type of part ground. Finally, the third group of errors is in the range of 1 µm or superior, which is classified as very bad ones. Based on these analyses, we can see in Figure 8 that for many conditions, neural network had a hundred percent of success or almost that, mainly in the region from 5 µm of depth of cut to 30 µm, proving a total efficiency of the system for certain processes.
Farah, and M. Sellami. (2004). Arabic Words Recognition With Classifiers Combination: An Application to Literal Amounts. Proc. Artificial Intelligence: Methodology, Systems and Applications, pp. 420-429. R. C. Gonzalez, R. E. Woods, S. L. Eddins. (2003). Digital Image Processing Using MATLAB. Prentice Hall; 1st edition, September 5, 2003 R. Safabakhsh and P. Adibi. (2005). Nastaaligh Handwritten Word Recognition Using a Continuous-Density variable-Duration HMM. The Arabian J. , vol. 30, pp. 95-118.