A neural network approach for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data

The present paper shows that a wide class of complex transfer functions encountered in geophysics can be efficiently modeled using neural networks. Neural networks can approximate numerical and nonnumerical transfer functions. They provide an optimum basis of nonlinear functions allowing a uniform approximation of any continuous function. Neural networks can also realize classification tasks. It is shown that the classifier mode is related to Bayes discriminant functions, which give the minimum error risk classification. This mode is useful for extracting information from an unknown process. These properties are applied to the ERS1 simulated scatterometer data. Compared to other methods, neural network solutions are the most skillful.