Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference...

this study investigates the prediction model of compressive strength of self–compacting concrete (scc) by utilizing soft computing techniques. the techniques consist of adaptive neuro–based fuzzy inference system (anfis), artificial neural network (ann) and the hybrid of particle swarm optimization with passive congregation (psopc) and anfis called psopc–anfis. their performances are comparativ...

  Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. In...

It is essential to have a uniform and calm flow field for a primary settling tank with high performance. In other words reducing the kinetic energy of the flow inside the settling tank can improve significantly the efficiency of these tanks and give opportunity to particles to settle in the settling zone of the tank. So determination of the velocity magnitude in various positions can be a good ...

This paper presents an approach to machine fault diagnosis and condition prognosis based on classification and regression tress (CART) and neuro-fuzzy inference systems (ANFIS). In case of diagnosis, CART, which is one of the decision tree methods, is used as a feature selection tool to select pertinent features from data set and ANFIS is used as a classifier. The crisp rules obtained from CART...

Measurements of temperatures and flows and pressures are used to estimate the dry point of the product for the distillation column. The Problem is characterized by the model complication and the strong colinearity between the measurements. In this article, the distributed RBF neural network (DRBFN) and principal component analysis (PCA) are used to develop the soft sensor (PCA-DRBFN model), and...

In this paper a method based on kernel principal component analysis (KPCA) and mixed kernel least square support vector machine regression (MKLS-SVM) for online quality prediction in atmospheric distillation column is presented. Firstly, the KPCA is employed to reduce the input vector’s dimensions of the multiple-input multiple-output (MIMO) soft sensor and created the data set which required t...

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...