Neural networks in ocean engineering

Abstract The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the research studies reported so far in these areas. It is found that, in general, neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods. The relative advantages of the neural network schemes proposed by various investigators are improved accuracy, lesser complexity in modeling and hence smaller computational effort and time, reduced data requirement in some cases, and so on. Neural networks have a very high degree of freedom, and that comes as handy while training it with examples. Exploration of more areas of application, implementation of advanced and hybrid forms of networks together with interpretation of the information contained in a trained network should receive more focus in future. Similarly the current difficulties in dealing with very large variations in the input, large warning times, extreme value predictions, and extrapolation beyond the observed range would have to be addressed in the near future.

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