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基于广义回归神经网络集成的宽带波束形成算法
引用本文:张贞凯,田雨波,周建江.基于广义回归神经网络集成的宽带波束形成算法[J].宇航学报,2012,33(8):1127-1131.
作者姓名:张贞凯  田雨波  周建江
作者单位:1. 江苏科技大学电子信息学院,江苏镇江212003;南京航空航天大学电子信息工程学院,南京210016
2. 江苏科技大学电子信息学院,江苏镇江,212003
3. 南京航空航天大学电子信息工程学院,南京,210016
基金项目:江苏高校优势学科建设工程资助项目,船舶工业国防科技预研基金项目,国家自然科学基金,江苏省研究生培养创新工程
摘    要:首先构造宽带波束成形所需要的协方差矩阵,利用基于粒子群优化算法对核主成分分析方法和广义回归神经网络进行了优化。在对神经网络的输入变量进行降维处理后,生成多个复杂度低的泛回归神经网络模型。利用提出的基于聚类启发式集成算法求出波束成形时的权系数,既考虑了网络的差异性,又考虑了网络的正确性。仿真结果表明,提出的基于聚类启发式神经网络集成的波束形成算法在网络结构十分简单的情况下,仍然具有较好的性能。

关 键 词:神经网络集成  波束形成  粒子群  模糊聚类  广义回归神经网络

A Novel Wideband Beam Forming Method Based Generalized Regression Neural Network Ensemble
ZHANG Zhen-kai , TIAN Yu-bo , ZHOU Jian-jiang.A Novel Wideband Beam Forming Method Based Generalized Regression Neural Network Ensemble[J].Journal of Astronautics,2012,33(8):1127-1131.
Authors:ZHANG Zhen-kai  TIAN Yu-bo  ZHOU Jian-jiang
Institution:1.Department of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.Department of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
Abstract:First,kernel principal component analysis(KPCA) method and the generalized regression neural network(GRNN) are optimized by using the particle swarm optimization(PSO) algorithm after the covariance matrix for beam forming is obtained.Second,optimized KPCA method is used to reduce the dimension of train samples in order to reduce the complexity of GRNN.Finally,considering both difference and correctness of every neural network weight coefficients for beam-forming are obtained by using the proposed neural network ensemble method based fuzzy clustering method(FCM) and Heuristic idea.The simulation results show that the proposed method has good performance under a very simple structure of the neural network.
Keywords:Neural network ensemble  Beam forming  Particle swarm optimization  Fuzzy clustering method  Generalized regression neural network(GRNN)
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