Abstract
Hereby we present the construction and usage of “Weak Connectiou”(WeCo) on Neural Networks(NN). We will show how these parallelization hypothesis increases the final system flexibility. The net design is based on standard procedures, but changed accordingly to WeCo parallelization principles. WeCo means parallelization with less weight on communication systems, as in: fine, medium and coarse grain parallelism, or between the parts of the implementation program. WeCo lays in-between parallel computers and sequential machines, building the bridge between them.
more specifically, Stock Exchange(SE) forecasting
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Cristea, A.I., Okamoto, T. (1997). A parallelization method for neural networks with weak connection design. In: Polychronopoulos, C., Joe, K., Araki, K., Amamiya, M. (eds) High Performance Computing. ISHPC 1997. Lecture Notes in Computer Science, vol 1336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024235
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DOI: https://doi.org/10.1007/BFb0024235
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