Abstract
In data acquisition and digital instrumentation fields, it is essential to understand the learning and recognition to acquire data and information of objects to be studied. In recent years, engineering modelling and simulation contribute greatly to the understanding of intelligent learning and recognition problems. The ability to learn is one of the central features of intelligence, which makes it an important concern for both cognitive psychology and artificial intelligence. In this paper, definitions and modelling aspects of learning are discussed. Fundamentals of learning and recognition and their applications are investigated and described. Illustrations are given to demonstrate the increasing applications of learning and recognition with engineering modelling in data acquisition and digital instrumentation fields.
Recommended Citation
Qin, Jun and Xu, Lin. "Data Acquisition and digital Instrumentation Engineering Modelling for Intelligent Learning and Recognition." Biosensors Journal 5, No. 1 (Spring 2015): 1-4. doi:10.4172/2090-4967.1000e103.
Comments
Copyright: © 2015 Qin J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.