This paper shows a three-phase series resonant DC-DC boost converter simulation model with double LLC tanks (SRC-W-DLLC-RTs) for hybrid solar wind system (HSWS) battery energy storage DC load. An intrinsic drawback of solar-wind technology is its intermittent and weather-dependent output voltage. MPPT control algorithm-based converter, which offers interfaces between load, can overcome this iss...

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using daily price indices with 2026 observations from October 2011 December 2019. employs a nonlinear spectral maximum overlapping discrete wavelet transform (MODWT) five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), Coiflet...

Interest in system identification especially for nonlinear systems has significantly increased in the past few decades. Soft-computing methods which concern computation in an imprecise environment have gained significant attention amid widening studies of explicit mathematical modelling. In this research, an adaptive neuro-fuzzy inference system (ANFIS) network design is deployed and used for m...

Self-compacting concrete is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction even in congested reinforcement without segregation and bleeding. In the present study self compacting concrete mixes were developed using blend of fly ash and rice husk ash. Fresh proper...

Abstract Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role environmental science design engineering structures are vital for watershed management. Since different parameters such as rainfall, temperature, discharge with the lag times significant effects on SSL, quantifying understanding nonlinear interactions dynamics has always been a challenge....

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Us...

In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained wit...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

Prediction of student’s performance is potentially important for educational institutions to assist the students in improving their academic performance, and deliver high quality education. Developing an accurate student’s performance prediction model is challenging task. This paper employs the Adaptive NeuroFuzzy Inference system (ANFIS) for student academic performance prediction to help stud...