This study proposes and validates a construction concept for the realization of a real-valued single-hidden layer feed-forward neural network (SLFN) with continuous-valued hidden nodes for arbitrary mapping problems. The proposed construction concept says that for a specific application problem, the upper bound on the number of used hidden nodes depends on the characteristic of adopted SLFN and the observed properties of collected data samples. A positive validation result is obtained from the experiment of applying the construction concept to the m-bit parity problem learned by constructing two types of SLFN network solutions. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Tsaih, R. H., & Wan, Y. W. (2009). A guide for the upper bound on the number of continuous-valued hidden nodes of a feed-forward network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5768 LNCS, pp. 658–667). https://doi.org/10.1007/978-3-642-04274-4_68
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