In this paper a new strategy is introduced for constructing a multi-hidden-layer feedforward neural network (FNN) where each hidden unit employs a polynomial function for its activation function that is different from other units. The proposed scheme incorporates a structure level adaptation as well as a function level adaptation methodologies in constructing the desired network. The activation functions considered consist of orthonormal Hermite polynomials. Using this strategy, a FNN can be constructed as having as many hidden layers and hidden units as dictated by the complexity of the problem being considered. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ma, L., & Khorasani, K. (2008). An adaptively constructing multilayer feedforward neural networks using hermite polynomials. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5227 LNAI, pp. 642–653). https://doi.org/10.1007/978-3-540-85984-0_77
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