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Sigmoid Generators for Neural Computing Using Piecewise Approximations
September 1996 (vol. 45 no. 9)
pp. 1045-1049

Abstract—A piecewise second order approximation scheme is proposed for computing the sigmoid function. The scheme provides high performance with low implementation cost; thus, it is suitable for hardwired cost effective neural emulators. It is shown that an implementation of the sigmoid generator outperforms, in both precision and speed, existing schemes using a bit serial pipelined implementation. The proposed generator requires one multiplication, no look-up table and no addition. It has been estimated that the sigmoid output is generated with a maximum computation delay of 21 bit serial machine cycles representing a speedup of 1.57 to 2.23 over other proposals.

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Index Terms:
Nonlinear function generators, sigmoid function, piecewise approximations, neural networks, hardware for twos complement notation, error analysis.
Citation:
Ming Zhang, Stamatis Vassiliadis, José G. Delgado-Frias, "Sigmoid Generators for Neural Computing Using Piecewise Approximations," IEEE Transactions on Computers, vol. 45, no. 9, pp. 1045-1049, Sept. 1996, doi:10.1109/12.537127
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