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Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties...
The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating, simple architecture, easily processing and hardware implementation. In the training phase, the disadvantage of some CMAC models with a larger fixed learning rate is the unstable phenomenon. The smaller learning rate would cause slower...
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