title: CATSMLP: toward a robust and interpretable multilayer perceptron with sigmoid activation functions.
abstract: Enhancing the robustness and interpretability of a multilayer perceptron (MLP) with a sigmoid activation function is a challenging topic. As a particular MLP, additive TS-type MLP (ATSMLP) can be interpreted based on single-stage fuzzy IF-THEN rules, but its robustness will be degraded with the increase in the number of intermediate layers. This paper presents a new MLP model called cascaded ATSMLP (CATSMLP), where the ATSMLPs are organized in a cascaded way. The proposed CATSMLP is a universal approximator and is also proven to be functionally equivalent to a fuzzy inference system based on syllogistic fuzzy reasoning. Therefore, the CATSMLP may be interpreted based on syllogistic fuzzy reasoning in a theoretical sense. Meanwhile, due to the fact that syllogistic fuzzy reasoning has distinctive advantage over single-stage IF-THEN fuzzy reasoning in robustness, this paper proves in an indirect way that the CATSMLP is more robust than the ATSMLP in an upper-bound sense. Several experiments were conducted to confirm such a claim.
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong. cskchung@comp.polyu.edu.hk
authors.forename: Fu-Lai
authors.initials: FL
authors.lastname: Chung
authors.forename: Shitong
authors.initials: S
authors.lastname: Wang
authors.forename: Zhaohong
authors.initials: Z
authors.lastname: Deng
authors.forename: Dewen
authors.initials: D
authors.lastname: Hu