A study of neural network architecture for weak non-linear modeling

This paper studies a property of neural network architecture for non-linear modeling. This method was proposed in our previous work and has three improvements; 1) the design of a sigmoidal function with localized derivative, 2) a deterministic scheme for weight initialization, and 3) an updating rule for weight parameters. We discuss its robustness against noise based on simulation results.