In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...

We propose a new estimation procedure for covariate adjusted nonlinear regression models for situations where both the predictors and response in a nonlinear regression model are not directly observed, however distorted versions of the predictors and response are observed. The distorted versions are assumed to be contaminated with a multiplicative factor that is determined by the value of an un...

This lab is about two topics in modeling. Interaction refers to a particular way of structuring models; it is a concept that applies to a wide variety of models. Logistic Regression is a form of nonlinear modeling that is particularly useful when the response variable is binomial, e.g., yes or no, alive or dead, success or failure. The two concepts are brought together in this lab only because ...

Base isolation is an increasingly applied earthquake-resistant design technique in highly seismic areas. Examination of the actual performance of isolated structures in real earthquake has become a critical issue. In this paper, a new computational method for system identification is proposed for obtaining insight into the linear and nonlinear structural properties of based-isolated buildings. ...

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that adhoc quadratic multi-level regression models can have finite-time blow up of statistical solutions and/or pathological behavior of their invariant measure. Here a new...

SUMMARY Linear and squared residual plots are proposed to assess nonlinearity and heteroscedasticity in regression diagnostics. It is shown that linear residual plots are useful for diagnosing nonlinearity and squared residual plots are powerful for detecting nonconstant variance. A paradigm for the graphical interpretation of residual plots is presented.

A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method C. Jiménez, P. Eriksson, V. O. John, and S. A. Buehler Institute of Atmospheric and Environmental Science, The University of Edinburgh, Edinburgh, UK Department of Radio and Space Science, Chalmers University of Technology, Gothenburg, Sweden Institute of Environ...