Recurrent Adaptive Neuro-Fuzzy Models for Predicting Time Series with Fuzzy Trends

Through the study of the residuals in the Lagrange Multiplier testing framework we devise a hy- pothesis test which allows us to determine if the residual time series is

A complete, first-of-its-kind chemical characterization of NIST PS1 Primary Standard for quantitative NMR (benzoic acid) has confidently established a direct realization of SI units

Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-

In accordance with the definition of risk as an emergency situation (ES), the fire risk is the risk measure characterizing the possibility of a forest fire in a certain area

As part of this work, experiments were conducted to search for anomalous situations and search for possible faulty helicopter units using the developed approach to the integration

To solve the problem of working with semistructured data, which often take place in the tasks of forecasting time series, it is proposed to use deep neural network

The task of such a scale can be solved only by developing common "rules of the game" in the software implementation of intelligent models, namely,

Proton Exchange Membrane fuel cell degradation, Prognostic and Health Management, Time-series prediction, Adaptive Neuro-Fuzzy Inference System.. [*] Corresponding