ARTIFICAL NEURAL NETWORKS: The New Era Of I.T
Journal: INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY (Vol.2, No. 1)Publication Date: 2012-25-12
Authors : Harmandeep Singh Assistant;
Page : 86-88
Keywords : ;
Abstract
Artificial neural networks are composed of interconnecting artificial neurons. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.
Other Latest Articles
- Knowledge and Information Management: Effective System for Organizational Growth
- Mental representations of sixth graders in Greece for the mechanism of vision in conditions of day and night
- Modeling Real Time Scheduler in OOAD Using UML
- EDUCATIONAL REFORMS IN SCHOOL EDUCATION AND THEIR IMPLICATIONS ON TEACHER EDUCATION
- EFFECT OF VEDIC MATHEMATICS ON ACHIEVEMENT IN MATHEMATICS AMONG FIFTH GRADE STUDENTS
Last modified: 2014-07-09 17:25:47