An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and...

The saturation percentage (SP) of soils is an important index in hydrological studies. In this paper, artificial neural networks (ANNs), multiple regression (MR), and adaptive neural-based fuzzy inference system (ANFIS) were used for estimation of saturation percentage of soils collected from Boukan region in the northwestern part of Iran. Percent clay, silt, sand and organic carbon (OC) were u...

Abstract The chlorine and total trihalomethane (TTHM) concentrations are sparsely measured in the trunk network of Bogotá, Colombia, which leads to a high uncertainty level at an operational level. For this reason, research assessed prediction accuracy for TTHM two black-box models based on following artificial intelligence techniques: neural networks (ANNs) adaptive neuro-fuzzy inference syste...

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decom...

Soft sensor is an effective tool to estimate industrial process variables which are hard to be measured online for the technical or economical reasons. The modeling methods of the sensor are related to the approximating precision and speed. A soft sensor model with rough set and Least Squares Support Vector Machines (LSSVM) is presented in the paper. The rough set is employed to compress the da...

Abstract This paper investigates different approaches to develop soft sensors from multi-rate sampled data. The data lifting approach consists of two steps, identifying a model with a slow/lifted sampling period and extracting a fast model. Approaches based on direct extraction and linear regression are briefly reviewed, followed by reformulating the task as an unconstrained optimization proble...

This paper presents methodologies to select equities based on soft-computing models which focus on applying fundamental analysis for equities screening. This paper compares the performance of three soft-computing models, namely multi-layer perceptrons (MLP), adaptive neuro-fuzzy inference systems (ANFIS) and general growing and pruning radial basis function (GGAP-RBF). It studies their computat...

In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase a...

*Correspondence: [email protected] 1Centre de Recerca Matemàtica, Bellaterra, Barcelona, 08193, Spain 2Mathematics Applications Consortium for Science and Industry, University of Limerick, Limerick, Ireland Full list of author information is available at the end of the article †Equal contributors Abstract A soft sensor for measuring product quality in the Bayer process has been developed. The sof...