Genetic Optimization Of Radial Basis Probabilistic Neural Networks
Genetic Optimization Of Radial Basis Probabilistic Neural Networks
复制标题
DOI：
10.1142/s0218001404003824
复制
发表时间：
2004-12
期刊：
影响因子：
--
通讯作者：
Wen-Bo Zhao;De-shuang Huang;Ji-Yan Du;Li-Ming Wang
中科院分区：
文献类型：
--
作者：
Wen-Bo Zhao;De-shuang Huang;Ji-Yan Du;Li-Ming Wang
讨论了用遗传算法优化径向基概率神经网络（RBPNN）的结构，包括第一隐层隐层中心的选取和高斯核函数控制参数的确定。在构造遗传算法的过程中，提出了一种新的编码方法来优化RBPNN的结构。该编码方法不仅能使选择的隐中心充分反映训练样本空间的密钥分布特征，使隐中心数目尽可能少，而且能同时确定与选择的隐中心相匹配的高斯核函数的最佳控制参数。此外，我们还建设性地提出了一个新的适应度函数，使所设计的RBPNN在不损失网络性能的情况下，在网络结构上尽可能简单。最后，以双螺旋判别问题和虹膜数据分类两个基准问题为例，对所设计的遗传算法进行了测试和评价。实验结果表明，与递归正交最小二乘算法（ROLSA）和改进的K-means算法（MKA）相比，所设计的遗传算法可以显著减少所需的隐中心数.特别是，通过统计实验证明，我们设计的GA优化RBPNN，仍然有一个更好的泛化性能相对于ROLSA和MKA，尽管网络规模已大大减少。此外，我们的实验结果也表明，我们设计的遗传算法也适用于优化径向基函数神经网络（RBFNN）。
This paper discusses using genetic algorithms (GA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing the genetic algorithm, a novel encoding method is proposed for optimizing the RBPNN structure. This encoding method can not only make the selected hidden centers sufficiently reflect the key distribution characteristic in the space of training samples set and reduce the hidden centers number as few as possible, but also simultaneously determine the optimum controlling parameters of Gaussian kernel functions matching the selected hidden centers. Additionally, we also constructively propose a new fitness function so as to make the designed RBPNN as simple as possible in the network structure in the case of not losing the network performance. Finally, we take the two benchmark problems of discriminating two-spiral problem and classifying the iris data, for example, to test and evaluate this designed GA. The experimental results illustrate that our designed GA can significantly reduce the required hidden centers number, compared with the recursive orthogonal least square algorithm (ROLSA) and the modified K-means algorithm (MKA). In particular, by means of statistical experiments it was proved that the optimized RBPNN by our designed GA, have still a better generalization performance with respect to the ones by the ROLSA and the MKA, in spite of the network scale having been greatly reduced. Additionally, our experimental results also demonstrate that our designed GA is also suitable for optimizing the radial basis function neural networks (RBFNN).