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However, they sometimes produce poor results when applied to time-varying signal This paper addresses the general problem of processing processing such as seismic signal processing. The main time-varying signals with neural networks, and investigates reason for this is that most conventional neural networks their uses in integrated stratigraphic interpretation. A multilayer are based on McCulloch-Pitts (MP) type neurons. This feedforward network is constructed directly from the neuron model is a simple logic element, completing a snap Caianiello's neuronic model, and its back propagation algorithm shot representation where a dynamic pattern is transferred is derived.
Very An absorbing boundary scheme using an infinite element high reflectivities appear for waves traveling obliquely to algorithm has been given for elastic wave modeling by the boundary. The boundary conditions based on the solving the boundary (or volume) integral equations related schemes of Lindman (1975) and Randall (1988) can absorb to the free space Green's functions. The integral equations obliquely incident waves to a great extent, but is not convenient have the ability to include infinite domains. We place an to be used in solutions of the BI or VI equation, infinite element at the end point of the boundary extending particularly for the VI equation where the unknown is only to infinity instead of the artificial boundary at the edge of the displacement, without the normal gradient or stress* domain of computation. Then infinite shape functions are involved. Some limitations and assumptions are necessary constructed for the infinite element.