Application of RBFNN Heuristic Training Method in Human Brain CT Images

Jing Zhou; Hongbo Shao; Ruihong Wang

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Application of RBFNN Heuristic Training Method in Human Brain CT Images

机译：RBFNN启发式训练方法在人脑CT图像中的应用

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With the rapid development of computer and network technology, the pattern classification technology is widely applied in the field of artificial intelligence, this paper introduces the radial basis function neural network (RBFNN) and local generalization error model. Since the expressions of the model's results contain integral operation, so the computation is of high complexity. And we can only apply it in the RBFNN's structure selection problem under the condition that the characteristics dimension of the sample is large. . In this paper, we give a heuristic method to training RBFNN based on local generalization error model, we can apply the method no matter when the characteristics dimension of the sample is large or not too large. At last, we apply the heuristic method in the brain CT image data, the experiment shows that computation time and the training precision are very satisfying.

机译：随着计算机和网络技术的飞速发展，模式分类技术在人工智能领域得到了广泛的应用，本文介绍了径向基函数神经网络（RBFNN）和局部泛化误差模型。由于模型结果的表达式包含积分运算，因此计算具有很高的复杂性。而且，只有在样本特征量较大的情况下，才能将其应用于RBFNN的结构选择问题。。本文提出了一种基于局部泛化误差模型的RBFNN训练启发式方法，无论样本的特征维数较大还是不太大，都可以采用该方法。最后将启发式方法应用于脑部CT图像数据中，实验表明该算法的计算时间和训练精度均令人满意。

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《Journal of Residuals Science & Technology》 |2016年第6期|共页
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Jing Zhou; Hongbo Shao; Ruihong Wang;
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中图分类废物处理与综合利用;
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Application of RBFNN Heuristic Training Method in Human Brain CT Images

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