the control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. there are varieties of problems in chemical engineering that can be formulated as nonlinear programming (nlps). the quality of the developed solution significantly affects the performance ...

in this paper, a sensitivity analysis of artificial neural networks (nns) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. to evaluate the accuracy of the proposed nn model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. the ...

suspended particles have deleterious effects on human health and one of the reasons why tehran is effected is its geographically location of air pollution. one of the most important ways to reduce air pollution is to predict the concentration of pollutants. this paper proposed a hybrid method to predict the air pollution in tehran based on particulate matter less than 10 microns (pm10), and the...

in this study, several artificial neural networks (anns) were developed to estimate seed and grain corn yields in parsabad moghan, iran. the data was collected by a face-to-face interview method from 144 corn farms during 2011. the energy ratios for seed and grain corns were calculated as 0.89 and 2.65, respectively. several multilayer perceptron anns with six neurons in the input layer and one...

Nowadays, firms apply the merger and acquisition strategy for gaining synergy, increasing the wealth of stockholders, economics of scales, enhancing efficiency, increasing the ability to research and develop, developing the firm and decreasing the risk. Developing an optimized model with the ability to identify the effective variables on the merger and acquisition process has a significant ...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...