Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

Structural equation modeling originated (Jöreskog (1973), Bentler (1980), Bollen (1989)) as a method for modeling linear relations among observed and hypothesized latent variables. Despite limitation inherent in the linearity assumption of traditional structural equation modeling, it has indeed provided a revolutionary and popular framework for addressing research questions in the social, psych...

We introduce a new class of non-linear models for functional data based on neural networks. Deep learning has been very successful in modeling, but there little work done the setting. propose two variations our framework: network with continuous hidden layers, called Functional Direct Neural Network (FDNN), and second version that utilizes basis expansions Basis (FBNN). Both are designed explic...