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...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and...

Many efforts have been made to model the the hot deformation (dynamic recrystallization) flow curves of different materials. Phenomenological constitutive models, physical-based constitutive models and artificial neural network (ANN) models are the main methods used for this purpose. However, there is no report on the modeling of warm deformation (dynamic spheroidization) flow curves of any kin...

Phenolic compounds are considered as priority pollutants because of their high toxicity at low concentration. In the present study, the sorption of p-chlorophenol (p-CP) by dried activated sludge was investigated. Activated sludge was collected as slurry from the sludge return line of a municipal wastewater treatment plant. Sorption experiments were carried out in batch mode. In order to invest...

The neuron state modeling and the local field modeling provides two fundamental modeling approaches to neural network research, based on which a neural network system can be called either as a static neural network model or as a local field neural network model. These two models are theoretically compared in terms of their trajectory transformation property, equilibrium correspondence property,...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...