Fuzzy logic alternative for analysis in the biomedical sciences
Computers and Biomedical Research
Applied Neural Networks For Signal Processing
Applied Neural Networks For Signal Processing
Computers in Biology and Medicine
Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals
Computers in Biology and Medicine
Automatic detection of ophthalmic artery stenosis using the adaptive neuro-fuzzy inference system
Engineering Applications of Artificial Intelligence
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Modeling of the angle of shearing resistance of soils using soft computing systems
Expert Systems with Applications: An International Journal
Prediction of building energy needs in early stage of design by using ANFIS
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for the prediction of early heat of hydration of plain and blended cements. Two different type of model is trained and tested using these data. The data used in these models are arranged in a format of five input parameters that cover the additives percentage (AP), grinding type (GT) and finesses of cements (FC) and an output parameter which is heat of hydration of cements (HHC). The results showed that neuro-fuzzy models have strong potential as a feasible tool for evaluation of the effect of additives percentage, grinding type (GT) and finesses of cements on the early heat of hydration of cements. Some conclusions concerning the impacts of features on the prediction of early heat of hydration of plain and blended cements were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modelling approach outperforms ANN model in terms of training performances and prediction accuracies. The results show that the proposed ANFIS model can be used in the prediction of early heat of hydration of plain and blended cements.