基于泛函連接網(wǎng)絡(luò)和差分進化算法的后非線性混疊信號盲分離方法
基金項目:
國家自然科學(xué)基金（60274006）,國家杰出青年基金（60325310）, 中國博士后科學(xué)基金（2003034062）, 廣東省自然科學(xué)基金博士科研啟動基金(04300015),廣東省教育廳自然科學(xué)研究項目,廣州市科技計劃項目(2004J1-C0323)和廣州市屬高?？萍加媱濏椖?2055)資助課題
Blind Source Separation of Nonlinear Mixtures Based on Functional Link Artificial Neural Networks and Differential Evolution Algorithm
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摘要: 把后非線性混疊信號盲分離的分離系統(tǒng)用泛函連接網(wǎng)絡(luò)來建模，對分離系統(tǒng)的輸出應(yīng)用高階統(tǒng)計量獨立性準(zhǔn)則作為測度，然后利用差分進化算法對泛函連接網(wǎng)絡(luò)的權(quán)值進行學(xué)習(xí)，從而獲得了一種后非線性混疊信號盲分離算法。由于泛函連接網(wǎng)絡(luò)是一種單層神經(jīng)網(wǎng)絡(luò)，具有學(xué)習(xí)參數(shù)少、收斂速度快和非線性逼近能力強的特點；而差分進化算法控制參數(shù)少、易于選擇、具有全局尋優(yōu)能力和快速的收斂特性；因而與其它的后非線性混疊信號盲分離方法相比，該文提出的分離算法具有計算簡單、收斂速度快、較高的精度和穩(wěn)定性好的特點。仿真結(jié)果顯示了這種方法是可行和有效的。Abstract: In this paper, a post nonlinear blind sources separation method is proposed. The demixing system of the post nonlinear mixtures is modeled using a functional link artificial neural network whose weights can be determined under the criterion of independence of its outputs. A criterion of independence based on higher order statistics is used to measure the statistical dependence of the outputs of the demixing system, and the differential evolution algorithm is utilized to minimize the criterion. The proposed method takes advantage of less learning parameters, high learning convergence rate of parameters, nonlinear approximation capability of the functional link artificial neural network, and few easily chosen control parameters, global optimization capability of the differential evolution algorithm. Compared to conventional post nonlinear blind sources separation approaches, the proposed approach for post-nonlinear blind source separation is characterized by less computational load, high convergence rate, high accuracy and robustness. Simulation results show that the proposed approach is capable of separating independent sources from their post-nonlinear mixtures.
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