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Artificial neural networks belong to the field of cognitive sciences which pertains to the study and applicability of the adaptive nature of human brain in solving real world problems. The architecture of a neural network system is built on a learning algorithm which can learn and adapt to input data parameters. They can be classified as nonlinear data modeling and decision making units which help in recognizing an existing hidden pattern among numerous well defined, classified data sets. The present day artificial neural network is a synergy of learning, training and prediction performed on…mehr

Produktbeschreibung
Artificial neural networks belong to the field of cognitive sciences which pertains to the study and applicability of the adaptive nature of human brain in solving real world problems. The architecture of a neural network system is built on a learning algorithm which can learn and adapt to input data parameters. They can be classified as nonlinear data modeling and decision making units which help in recognizing an existing hidden pattern among numerous well defined, classified data sets. The present day artificial neural network is a synergy of learning, training and prediction performed on high precision computers, based on an underlying computational model which is capable of processing large amounts of data. Hardware advances and an increase in computing power have enhanced the practicable uses of neural network based systems. Neural networks are used in weather forecasting, predicting financial disasters etc. The ability of the neural network in prior decision making is usedin credit rating analysis, targeted marketing, and internet search engines.
Autorenporträt
Suresh Jagirdar is a knowledge expert in IT networking and computing industries. He has tremendous expertise in artificial neural network technologies applied to statistical computing. This book is an effort to emphasize the growing need to augment neural networks and cognitive sciences in day to day computing.