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

2019 ◽  
Vol 32 (12) ◽  
pp. 8725-8734 ◽  
Author(s):  
Tien-Loc Le ◽  
Tuan-Tu Huynh ◽  
Chih-Min Lin
Keyword(s):  
Neural Network ◽  
Adaptive Filter ◽  
Filter Design ◽  
Noise Cancellation ◽  
Emotional Learning ◽  
Active Noise Cancellation ◽  
Active Noise ◽  
Brain Emotional Learning ◽  
Recurrent Type

Prediction filter design for active noise cancellation headphones

2013 ◽  
Vol 7 (6) ◽  
pp. 497-504 ◽  
Author(s):  
Markus Guldenschuh ◽  
Robert Höldrich
Keyword(s):  
Filter Design ◽  
Noise Cancellation ◽  
Active Noise Cancellation ◽  
Active Noise ◽  
Prediction Filter

scholarly journals Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongmei Li ◽  
Di Li ◽  
Xiangjian Chen ◽  
Zhongqiang Pan
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Network Model ◽  
Emotional Learning ◽  
Rehabilitation Robot ◽  
Inference System ◽  
Learning Network ◽  
Interval Type ◽  
Brain Emotional Learning

A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

Active Noise Cancellation using Adaptive Filter Algorithms

2018 ◽  
Vol V7 (02) ◽  
Author(s):  
B A Sujathakumari ◽  
Ravi S Barki ◽  
Lokesh A R ◽  
Pavithra C M ◽  
Keyword(s):  
Adaptive Filter ◽  
Noise Cancellation ◽  
Active Noise Cancellation ◽  
Active Noise ◽  
Filter Algorithms

scholarly journals Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Di Li ◽  
Xiangjian Chen
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Fuzzy Theory ◽  
Learning Model ◽  
Emotional Learning ◽  
Superior Performance ◽  
Learning Network ◽  
Proposed Model ◽  
Brain Emotional Learning

Emotion recognition plays a crucial role in human-robot emotional interaction applications, and the brain emotional learning model is one of several emotion recognition methods, but the learning rules of original brain emotional learning model play poor adaptation and do not work very well. In fact, existing facial emotion recognition methods do not have high accuracy and are not sufficiently practical in real-time applications. In order to solve this problem, this paper introduces an optimal model, which merges interval type-2 recurrent wavelet fuzzy system and brain emotional learning network for emotion recognition. The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. There are no rules initially, and then the structure and parameters of model are tuning online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. The proposed model could reduce the uncertainty in terms of vagueness by using type-2 recurrent wavelet fuzzy theory and removing noise samples. Finally, the superior performance of the proposed method is demonstrated by its comparison with some emotion recognition methods on five emotion databases.

Study of the Effects of Active Noise Cancellation on Music Playback

2021 ◽  
Author(s):  
Sattwik Basu ◽  
Jeffrey Tackett ◽  
David Trumpy ◽  
Adam Walt ◽  
Santosh Adari
Keyword(s):  
Noise Cancellation ◽  
Active Noise Cancellation ◽  
Active Noise
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