Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network

Simultaneous prediction of 10m and 100m wind speeds using a model inspired by brain emotional learning

Predicting wind speed simultaneously at multiple heights, particularly at 10 and 100 metres (m), presents unique challenges due to diverse influences. At lower altitudes, wind speed is significantly affected by surface factors including roughness, vegetation, and man-made structures, causing sharp fluctuations, while at higher altitudes, it is primarily influenced by atmospheric conditions, resulting in smoother flow patterns. Traditional models often require separate systems for each altitude, limiting their efficiency and accuracy. This study introduces the brain emotional learning based on basic and functional memories (BELBFM) model, inspired by adaptive emotional learning mechanisms in the mammalian brain, to predict wind speeds at both altitudes simultaneously. Using ERA5 reanalysis data, BELBFM effectively captures the nonlinear dynamics of wind behavior. Evaluation with data from the Persian Gulf demonstrates BELBFM's high accuracy, enhancing predictive capabilities for applications in renewable energy and structural engineering. This unified model provides a robust and efficient solution for adaptive wind forecasting.

A review on type-2 fuzzy neural networks for system identification

In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.