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Nonparametric adaptive kernel density estimator (NAKDE) based blind source separation(BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of source signal separation by BSS method, the probability distribution functions of source signals must be described as accurately as possible. Compared to the nonparametric fixed-width kernel...
Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed...
This paper proposes an improved natural gradient algorithm for blind source separation (BSS) based on the constrained optimization method. The improved algorithm introduces a scaling factor that restricts the training process by the balance spot, which adds little computational complexity and overcomes the conflict between the convergence rate and the steady-state accuracy. Therefore, the new algorithm...
BP network training algorithm is based on the error gradient descent to modify weights, which leads to the inevitable problem of a local minimum point. Some researchers have presented some amending ways and made some remarkable achievements. But combining others algorithm for adjusting the weights of BP network is few. At present, a new evolution algorithm called as differential evolution is used...
Blind Source Separation (BSS) problems generally can be simplified as an optimization model with orthogonal constraints. Addressing it, natural gradient algorithms are often used. But this kind of algorithm converges relatively slowly and the separation accuracy is sensitive to step size parameter. An adaptive Givens rotations algorithm is proposed in this paper in order to make faster convergence...
In this paper, we present several dynamical systems for efficient and accurate computation of optimal low rank approximation of a real matrix. The proposed dynamical systems are gradient flows or weighted gradient flows derived from unconstrained optimization of certain objective functions. These systems are then modified to obtain power-like methods for computing a few dominant singular triplets...
Generalized cross entropy estimator (GCEE) based nonparametric Blind Signal Separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distribution of source signals must be described as accurately as possible. Compared to the nonparametric fixed-width kernel density estimator...
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