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dc.contributor.authorMandal, S.
dc.date.accessioned2008-07-18T05:09:31Z
dc.date.available2008-07-18T05:09:31Z
dc.date.issued2003
dc.identifier.citationAICTE-ISTE sponsored short term training programme on coastal erosion areas (CEA) - Protection and management, 06-18, January, 2003. Lecture volume, 227-237p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1227
dc.description.abstractNeural networks have the ability to recognize the hidden pattern in the data and accordingly estimate the output values. Provision of model-free solutions, data error tolerance, built in dynamism and lack of any exogenous input requirement makes the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number of processing units. Its merit is the ability to deal with information whose interrelation is ambiquous or whose functional relation is not clear. Basically we need actual input-output data of any particular process/system, which will be used to train the neural network. Once the network is trained, it trained, it estimates/predicts the output for unknown input data without any assumptions. In many occasions, neural network predictions are much closer to target output and better than other numerical / empirical methods. That is why it is becoming popular in various fields including coastal engineering. Waves and tides will play important roles in coastal erosion or accretion. This paper briefly describes the back-propagation neural networks and its application towards prediction of waves and tides
dc.language.isoen
dc.publisherNational Institute of Technology Karnataka, India
dc.rightsCopyright [2003]. It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice that the rights are violated then the item would be withdrawn.
dc.subjectcoastal engineering
dc.subjectsurface water waves
dc.subjectcoastal erosion
dc.subjectaccretion
dc.subjectwave predicting
dc.subjectwave propagation
dc.titleApplication of neural networks in coastal engineering
dc.typeOther


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