Group method of data handling has been proven an effective knowledge mining tool to emerge the influencing factors of enterprises growth in the past researches, but when employed to analyze the small and micro enterpr...
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ISBN:
(纸本)9781479953769
Group method of data handling has been proven an effective knowledge mining tool to emerge the influencing factors of enterprises growth in the past researches, but when employed to analyze the small and micro enterprises in Sichuan Province, its effectiveness is reduced by the noise in the data obtained from the enterprises' produce and business operations monitor platform website. In order to increase the noise immunity of the method, the monitoring data is firstly processed as some symmetric triangular fuzzy numbers in this paper, and the parameter estimation technique for all self-organized models named partial functions in the method is changed from the former regression analysis with determined training data set to the fuzzy programming with fuzzy set. Basing on this transform, a group method of the symmetric triangular fuzzy numbers handling is presented and the result of its empirical study in Sichuan Province proves the method is able to disclose some key influencing factors of the small and micro enterprises' growth with unstable and noisy monitoring data.
In this paper we propose an approach for underdetermined blind separation in the case of additive Gaussian white noise and pink noise in addition to the most challenging case where the number of source signals is unkn...
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ISBN:
(纸本)9783319081564;9783319081557
In this paper we propose an approach for underdetermined blind separation in the case of additive Gaussian white noise and pink noise in addition to the most challenging case where the number of source signals is unknown. In addition to that, the proposed approach is applicable in the case of separating I + 3 source signals from I mixtures with an unknown number of source signals and the mixtures have additive two kinds of noises. This situation is more challenging and also more suitable to practical real world problems. Moreover, unlike to some traditional approaches, the sparsity conditions are not imposed. Firstly, the number of source signals is approximated and estimated using multiple source detection, followed by an algorithm for estimating the mixing matrix based on combining short time Fourier transform and rough-fuzzy clustering. Then, the mixed signals are normalized and the source signals are recovered using multi-layer modified Gradient descent Local Hierarchical Alternating Least Squares algorithm exploiting the number of source signals estimated, and the mixing matrix obtained as an input and initialized by multiplicative algorithm for matrix factorization based on alpha divergence. The computer simulation results show that the proposed approach can separate I + 3 source signals from I mixed signals, and it has superior evaluation performance compared to some traditional approaches in recent references.
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