A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks
A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks
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径向基概率神经网络的构造性混合结构优化方法
DOI：
10.1109/tnn.2008.2004370
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发表时间：
2008-12-01
影响因子：
--
通讯作者：
Du, Ji-Xiang
中科院分区：
文献类型：
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作者：
Huang, De-Shuang;Du, Ji-Xiang
本文提出了一种用于径向基概率神经网络（RBPNN）的新型启发式结构优化方法。首先，提出了一种最小体积覆盖超球（MVCH）算法来选择RBPNN的初始隐藏层中心，然后采用递归正交最小二乘算法（ROLSA）结合粒子群优化（PSO）算法进一步优化RBPNN的初始结构。通过八个基准分类问题以及两个实际应用问题，即涉及50种植物的植物物种识别任务和掌纹识别任务，对所提出的算法进行了评估。实验结果表明，我们所提出的算法对于RBPNN的结构优化是可行且高效的。在这两个任务中，RBPNN比多层感知器网络（MLPN）和径向基函数神经网络（RBFNN）实现了更高的识别率和更好的分类效率。此外，实验结果表明，在植物物种识别任务中，优化后的RBPNN的泛化性能明显优于优化后的RBFNN。
In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN.