background and objectives: evaluating the performance of clinical units is critical for effective managementof health settings. certain assessment of clinical variables for performance analysis is not always possible,calling for use of uncertainty theory. this study aimed to develop and evaluate an integrated independentcomponent analysis-fuzzy-data envelopment analysis approach to accurate the...

In this paper we propose a new technique focused on the selection of the important input variable for modelling complex systems of function approximation problems, in order to avoid the exponential increase in the complexity of the system that is usual when dealing with many input variables. The proposed parallel processing approach is composed of complete radial basis function neural networks ...

Ala~traet--Errors-in-variables problems are considered for the case of three variables, where the underlying relations among the noise-free variables are linear, and covariance information for the noisy variables is available. The static problem where the variables are complex (which can arise when narrowband filtering is used) is analysed in detail. Some results are also presented for the case...

In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems On the one hand this scheme can be used for static data when some of the input variables are missing On the other hand it can also be used for sequential data when some of the input variables are missing or are available at di erent frequencies Unlike in the cas...

this paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (ccfst) stub columns under axial loading condition based on artificial neural networks (anns) by using a large wide of experimental investigations. the input parameters were selected based on past studies such as outer diameter of column, compressive strength...

one of the new topics in dea is the data with integer values. in dea classic models, it is assumed that input and output variables have real values. however, in many cases, some inputs or outputs can have integer values. measuring cost efficiency is another method to evaluate the performance and assess the capabilities of a single decision-making unit for manufacturing current products at a min...

In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems. On the one hand, this scheme can be used for static data when some of the input variables are missing. On the other hand, it can also be used for sequential data, when some of the input variables are missing or are available at diierent frequencies. Unlike in t...

Choosing a useful combination of input variables and an appropriate complexity of the model is an essential task in nonlinear regression analysis because of the risk of overfitting. This article provides a workable solution for the multilayer perceptron model. An initial structure of the model, including all the input variables, is fixed in the beginning. Only the most useful input variables an...