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

Li, Hongmei; Li, Di; Chen, Xiangjian; Pan, Zhongqiang

doi:10.1155/2021/8892290

Cited by 3 publications

(2 citation statements)

References 40 publications

(43 reference statements)

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“…e main idea of PCA is to reduce the n-dimensional nature of the data to k-scale (k < n). In this way, the purpose of data reduction and feature extraction is realized [7][8][9]. e specific process of the PCA algorithm is as follows: suppose there is a data set s, including m samples, and the dimension of each sample is n, that is:…”

Section: D Model Construction Based On Image Processingmentioning

confidence: 99%

Image Processing Based on Fuzzy Mathematics Theory

Hou

2022

Security and Communication Networks

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In order to further improve the problems of poor rationality and weak antinoise ability of existing image processing algorithms and technical algorithms, an image processing research method based on fuzzy mathematical theory is proposed. First, aiming at the ill-posed problem of the PFCM algorithm, the neutrality and rejection degree are used to construct a regular term and embed the algorithm objective function to enhance the correlation between the attribute parameters of the fuzzy set of the sample graph, so as to solve the ill-posed problem of the PFCM algorithm. Secondly, in view of the same noise sensitivity problem of PFCM algorithm as a traditional fuzzy clustering algorithm, combined with the robust ideas of FCM_S1 and FCM_S2 algorithms, the objective function of robust segmentation algorithm for graph fuzzy clustering (RPFCM_s) is constructed. The misclassification rate of the clustering algorithm proposed in this study in image segmentation is reduced by 38%–76%, and the misclassification rate of the corresponding segmentation result of the ATPFCA algorithm is reduced by 5%–77%. Therefore, the algorithm not only improves the effective segmentation efficiency of the fuzzy mathematical theory algorithm for the processing of uneven grayscale images but also enhances the anti-noise robustness of the algorithm.

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Section: D Model Construction Based On Image Processingmentioning

confidence: 99%

Image Processing Based on Fuzzy Mathematics Theory

Hou

2022

Security and Communication Networks

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“…In [34], the type-2 recurrent fuzzy brain emotional learning network is used to achieve adaptive filter for the active noise cancellation. In [35], the data-driven control method is combined with the interval type-2 intuition fuzzy brain emotional learning network for the multiple degree-offreedom rehabilitation robot.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

2021

Mathematical Problems in Engineering

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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.

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