ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

yarn tenacity is one of the most important properties in yarn production. this paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. the artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 ne. as the first step for modeling, the empirical data is col...

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

estimation of the conceptual costs in construction projects can be regarded as an important issue in feasibility studies. this estimation has a major impact on the success of construction projects. indeed, this estimation supports the required information that can be employed in cost management and budgeting of these projects. the purpose of this paper is to introduce an intelligent model to im...

neural networks because of their abilities are used to patterns recognition. in statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...

in general, amount of sludge will definitely increase in near future and composting processes, optimum composting conditions and compost use as fertilizer and soil amendment will then be significant research topics. the present study was conducted for o2 parameter estimation by multiple regression and artificial neural networks methods. daily temperature, ch4, h2s, co2 and o2 measurements were ...

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...