Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computer-implementable form. There are many successful applications of this methodology to situations in which expert knowledge exist, the most well known is an application to fuzzy control. In some cases, fuzzy methodology is applied ev...

To answer the impacts under specific what-if scenarios together with simulation tools has been demanding in different environmental problems. In this contribution, a general software framework for time series scenario composition is proposed to deal with this issue. It is done through providing an interface to process available raw time series data and to compose scenarios of interest. These co...

in the present research, implementations of different computational geometry technologies in isogeometric analysis framework for computational fracture mechanics are investigated. nurbs and t-splines are two different computational geometry technologies which are studied in this work. among features of b-spline basis functions the possibility of enhancing a b-spline basis with discontinuities b...

There exist some processes difficult to control as the chemical ones, a common problem takes place when the output cannot be measured on-line, and so, closed-loop control cannot be implemented. In this work an iterative learning control type proportional-derivative is analyzed and theoretical results are shown, this control is applied to a biological reactor to degrade phenol by working in disc...

This paper presents a fuzzy stochastic dynamic programming (FSDP) approach to derive steady-state multipurpose reservoir operating policies. The vagueness associated with some operating objectives as well as with the decision-making process is apprehended through fuzzy set theory. Operating objectives are considered as fuzzy sets and their membership functions represent decision maker’s prefere...

Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high no...

This paper investigates two important analytical properties of hyperbolic-polynomial penalized splines, HP-splines for short. HP-splines, obtained by combining a special type difference penalty with B-splines (HB-splines), were recently introduced the authors as generalization P-splines. HB-splines are bell-shaped basis functions consisting segments made real exponentials eαx,e−αx and linear mu...

In this paper, ensemble models are developed to accurately forecast software reliability. Various statistical (multiple linear regression and multivariate adaptive regression splines) and intelligent techniques (backpropagation trained neural network, dynamic evolving neuro–fuzzy inference system and TreeNet) constitute the ensembles presented. Three linear ensembles and one non-linear ensemble...

A spline-based modification of the previously developed Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. In order to improve the approximation accuracy, cubic B-splines are substituted for triangular membership functions. The network is trained with a hybrid learning rule combining least squares estimation for the output layer and gradient descent for the hidden layer. The initialization of...