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We study unsupervised learning by developing a generative model built from progressively learned deep convolutional neural networks. The resulting generator is additionally a discriminator, capable of "introspection" in a sense — being able to self-evaluate the difference between its generated samples and the given training data. Through repeated discriminative learning, desirable properties...
In this paper, an effective hybrid optimization strategy by incorporating the metropolis acceptance criterion of Simulated Annealing (SA) into crossover operator of Genetic Algorithm (GA), is used to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA-SVR. The developed GASA-SVR model is being applied for monthly...
In this paper, simplified parameter-extraction process is presented for digital predistortion (DPD) design. The predistorter identification is based on the indirect learning architecture. In the proposed parameter-extraction procedure, the feedback data from PA output is normalized by its own peak value instead of expected gain of PA, and then this data is used for parameters extraction. The estimated...
To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months...
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