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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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