基于免疫克隆聚类协同神经网络的图像识别
doi: 10.3724/SP.J.1146.2007.00405
Image Recognition Using Synergetic Neural Networks Based on Immune Clonal Clustering
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摘要: 该文提出了基于免疫克隆聚类的协同神经网络原型向量求解算法，该算法充分利用免疫克隆的高效全局最优搜索能力构造数据聚类算法，将新聚类算法用于训练协同神经网络的原形向量，并对Brodatz纹理图像库以及合成孔径雷达图像目标进行识别。仿真实验结果表明，相比标准协同神经网络，该算法可以提高网络的识别性能，同经典的支撑向量机相比，该算法在识别率相当的情况下，样本的训练和测试时间都明显缩短。Abstract: A novel image recognition algorithm, Synergetic Neural Networks (SNN) based on immune clonal lgorithm, is proposed in this paper. The presented method introduces the global optimal searching ability of immune clonal select algorithm to construct data clustering algorithm, which used to solve the prototype vector in SNN. The simulation result of the Brodatz images and Synthetic Aperture Radar (SAR) images show the proposed algorithm can improve the performance of SNN as compared with the standard SNN and it can reduce greatly the training and test time leave the classification accuracy almost unchanged as compared with the traditional support vector machine.
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