in this paper, the problem of decentralized model reference adaptive control (mrac) for a class of large scale systems with time varying delay in interconnected term and input and state delays is studied. to compensate the effect of input delay indirectly, a smith predictor built on. to handle the effects of the time delays in input, the adaptive controller part includes two auxiliary dynamic f...

data envelopment analysis (dea) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (dmus) where multiple inputs and outputs exist. in the conventional applications of dea, the data are considered as specific numerical values with explicit designation of being an input or output. however, the observed values of the data are sometimes imprec...

forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...

the climate and weather patterns of buffalo (new york, u.s.a.) are strongly influenced by thecity’s proximity to lake erie. total monthly snowfall in buffalo is forecasted using neural network techniques(multi-layer perceptron = mlp) and a multiple linear regression (lr) model. the period of analysis comprises 28 years from january 1982 to december 2009. input data include: zonal wind speed (u-...

this paper describes a study of operational parameters by using the multivariate data analysis and neural networks for a municipal waste incinerator located in majorca (spain). the basis of the study also includes the chemometric techniques: linear multivariate regression to develop a model with certain predictive capabilities; linear principal component analysis, which allow the number of vari...

The case study is based on ISO 14001 and compares the validation with other certified industries. In view of the above, response has been collected to have further improvements through developed model which was prepared based on the factorization of various input and output variables which is linked to the clauses of ISO 14001. The developed model has represented almost all the clauses of ISO 1...

The selection of an appropriate subset of variables from a set of measured potential input variables for inclusion as inputs to model the system under investigation is a vital step in model development. This is particularly important in data driven techniques, such as artificial neural networks (ANNs) and fuzzy systems, as the performance of the final model is heavily dependent on the input var...

In order to model the distribution of input variables, a reasonable assumption is that input variables x are generated by some process u, and that process has a Gaussian error. Thus, the distribution of input variables can be presented as ppxq N pu,Σxq. The new data point for prediction will be annotated as x . In the general case, we predict on the entire set of points representing a single sn...

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily C...