artificial neural network
Yuvaranjitha.J,
Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications
ISSN: 2320-7248 Impact Factor:1.8 Volume:1 Issue:3 Year: 08 November,2013 Pages:44-51
Abstract
This document about Artificial Neural Network.An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. each node represents an artificial neuron and an each node has a connection from the output of one neuron to the input of another. In computer science and related fields, artificial neural networks are models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected " neurons" that can compute values from inputs by feeding information through the network.For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the network's designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read. Artificial Neural network contains terms of Mechanism. It Contains terms of methods. Those are back propagation, Activation function. Using this network we can perform more and more application.
Kewords
What is an ANN? Characteristics of ANN. Architecture of ANN. Training or Learning of ANN. Back propagation. How do neura
Reference
Dominic, S., Das, R., Whitley, D., Anderson, C. (July 1991). "Genetic reinforcement learning for neural networks".IJCNN-91-Seattle International Joint Conference on Neural Networks.IJCNN-91-Seattle International Joint Conference on Neural Networks. Seattle, Washington. [2] Gurney, K. (1997) An Introduction to Neural Networks London: Routledge. ISBN 1-85728-673-1 (hardback) or ISBN 1-85728-503-4 (paperback) [3] Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall, ISBN 0-13-273350-1 [4] Bhadeshia H. K. D. H. (1999). "Neural Networks in Materials Science".ISIJ International39 (10): 966–979. doi:10.2355/isijinternational.39.966 [5] Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Oxford: Oxford University Press. ISBN 0-19-853849-9 (hardback) or ISBN 0-19-853864-2 (paperback) [6] Lawrence, Jeanette (1994) Introduction to Neural Networks, California Scientific Software Press. ISBN 1-883157-00-5 [7] Masters, Timothy (1994) Signal and Image Processing with Neural Networks, John Wiley & Sons, Inc. ISBN 0-471-04963-8 [8] Ness, Erik. 2005. SPIDA-Web. Conservation in Practice 6(1):35-36. On the use of artificial neural networks in species taxonomy. [9] Ripley, Brian D. (1996) Pattern Recognition and Neural Networks, Cambridge [10] Siegelmann, H.T. and Sontag, E.D. (1994). Analog computation via neural networks, Theoretical Computer Science, v. 131, no. 2, pp. 331–360.electronic version [11] SergiosTheodoridis, KonstantinosKoutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN 978-1-59749-272-0. [12] Smith, Murray (1993) Neural Networks for Statistical Modeling, Van Nostrand Reinhold, ISBN 0-442-01310-8 [13] Wasserman, Philip (1993) Advanced Methods in Neural Computing, Van Nostrand Reinhold, ISBN 0-442-00461-3 [14] "http://en.wikipedia.org/w/index.php?title=Artificial_neural_network&oldid=573650068
