^ 1.01.1Han, Jun; Morag, Claudio. The influence of the sigmoid function parameters on the speed of backpropagation learning. Mira, José; Sandoval, Francisco (编). From Natural to Artificial Neural Computation. Lecture Notes in Computer Science 930. 1995: 195–201. ISBN 978-3-540-59497-0. doi:10.1007/3-540-59497-3_175.
Mitchell, Tom M. Machine Learning. WCB–McGraw–Hill. 1997. ISBN 0-07-042807-7.. In particular see "Chapter 4: Artificial Neural Networks" (in particular pp. 96–97) where Mitchell uses the word "logistic function" and the "sigmoid function" synonymously – this function he also calls the "squashing function" – and the sigmoid (aka logistic) function is used to compress the outputs of the "neurons" in multi-layer neural nets.
Humphrys, Mark. Continuous output, the sigmoid function. [2015-02-01]. (原始内容存档于2015-02-02). Properties of the sigmoid, including how it can shift along axes and how its domain may be transformed.