Objective: Soil temperature serves as a key variable in hydrological investigations to determine soil moisture content as well as hydrological balance in watersheds. The ingoing research aims to shed lights on potential of artificial neural networks (ANNs) and Neuro-Fuzzy inference system (ANFIS) to simulate soil temperature at 5-100 cm depths. To satisfy this end, climatic and...

support vector regression (svr) is a learning method based on the support vector machine (svm) that can be used for curve fitting and function estimation. in this paper, the ability of the nu-svr to predict the catalytic activity of the fischer-tropsch (ft) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (mlp) and subtractiv...

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. Howeve...

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptivenetwork-based fuzzy inference ...

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given no...

Despite the importance of dams for water distribution various uses, adequate forecasting on a day-to-day scale is still in great need intensive study worldwide. Machine learning models have had wide application resource studies and shown satisfactory results, including time series levels dam flows. In this study, neural network (NN) adaptive neuro-fuzzy inference systems (ANFIS) were generated ...

The dynamic Young’s modulus (E dyn ) is a parameter needed for optimizing different aspects related to oil well designing. Currently, E determined from the knowledge of formation bulk density, in addition shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system ...

Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel; design variables are the width and effective depth of the continuous beam and steel ratios for positive and nega...

introduction: the adaptive neuro-fuzzy inference system (anfis) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. in this study we used this model in breast cancer detection. methodology: a set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  first, the risk fact...