Prof. Xizhang Wei Profile

Prof. Xizhang Wei

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Proceedings Article | 8 November 2023 Paper

Discriminative infinite fuzzy restricted Boltzmann machine learning for HRRPs classification

Zechao Wang, Xizhang Wei

Proceedings Volume 12923, 1292307 (2023) https://doi.org/10.1117/12.3011401

KEYWORDS: Fuzzy logic, Machine learning, Radar, Target recognition, Performance modeling, Neural networks, Detection and tracking algorithms, Systems modeling, Algorithm development

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Deep learning architectures have been widely fostered throughout the last years and used in various applications, such as object recognition, image reconstruction, and signal processing. Recognition based on radar high resolution range profiles sequences(HRRPs) is essential to robust radar target recognition. Generally, it is almost always better to recognize radar targets in low-clutter environments. Recognition methods commonly cannot extract features from HRRP sequences, especially when the environmental background is not "clean." The Restricted Boltzmann Machine (RBM) is an excellent generative learning model. By extending its parameters from real numbers to fuzzy parameters and adding a hidden layer dynamic random permutation algorithm, we developed the discriminative infinite fuzzy restricted Boltzmann machine (Dis-iFRBM). The proposed model possesses better robust recognition capability than the discriminative infinitely restricted Boltzmann machine (Dis-iRBM). In order to verify the recognition stability and robustness of Dis-iFRBM, radar HRRP sequences recognition experiments are conducted in this paper. The radar HRRPs recognition experiments were conducted to validate the identity stability and robustness of Dis-iFRBM. The experimental results on several HRRPs datasets show that the proposed method is more effective in a "noisy" environment than other models. Moreover, the proposed recognition model have some significant advantages over the traditional structures

Proceedings Article | 14 June 2023 Paper

Reconstruction performance analysis of discriminative infinite fuzzy restricted Boltzmann machine

Zechao Wang, Xizhang Wei

Proceedings Volume 12708, 1270835 (2023) https://doi.org/10.1117/12.2683962

KEYWORDS: Image restoration, Fuzzy logic, Data modeling, Reconstruction algorithms, Neural networks, Internet of things, Synthetic aperture radar

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The existing traditional neural network reconstruction models have some questions, including the high training epochs, low recognition rate, and complex structure. The restricted Boltzmann machine (RBM) is an excellent generative learning model for feature extraction, which is a simple model compared to other deep neural networks. The Discriminative Fuzzy Restricted Boltzmann Machine (DFRBM) is proposed by extending its parameters from natural numbers to fuzzy ones. Then we introduced the random permutation (RP) algorithm, with the hidden units random permutation, Discriminative Infinite Fuzzy Restricted Boltzmann Machine (Dis-iFRBM) is proposed. Dis-iFRBM is a better RBM model than DFRBM and Classical RBMs.We further investigate and compare the generative ability of the Dis-iFRBM on image reconstruction. First, we transform the MSTAR SAR piece to HRRPs images. Then the Dis-iFRBM, DFRBM, and Classical RBMs are compared in detail under optimal conditions on the HRRPs images that transformed from MSTAR data sets. Specifically, they can be trained by relatively limited datasets into excellent stand alone classifiers and retain satisfactory generative capability simultaneously. The comparison of experimental images shows that the Dis-iFRBM possesses better generative capability than the Discriminative Fuzzy Restricted Boltzmann Machine (DFRBM). Meanwhile, surveillance images in the city, license plate number recognition, and other scenarios need damaged image restoration. Dis-iFRBM as a model can save computational resources of terminal image devices that deploy in the Urban Internet of Things. The experiment results indicate that the Dis-iFRBM outperforms image restoration. It can achieve smaller average reconstruction errors (AREs) while given a small number of hidden units. Finally, experimentation over several classical RBMs revealed the proposed approach’s preferable reconstruction capability.

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