An improved parameter learning methodology for RVFL based on pseudoinverse learners
An improved parameter learning methodology for RVFL based on pseudoinverse learners
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DOI：
10.1007/s00521-022-07824-y
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发表时间：
2022-10
影响因子：
6
通讯作者：
Xiaoxuan Sun;Xiaodan Deng;Qian Yin;Ping Guo
中科院分区：
文献类型：
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作者：
Xiaoxuan Sun;Xiaodan Deng;Qian Yin;Ping Guo
随机向量函数连接神经网络(RVFL)作为一种紧凑而有效的学习模型，已被证实具有普遍的逼近能力。它在各个领域都得到了相当大的关注。然而，RVFL中随机产生的参数往往会导致有效信息的丢失和数据的冗余，这在实践中严重降低了模型的性能。提出了一种基于伪逆学习的RVFL网络参数学习方法(RVFL-PL)。RVFL-PL不是直接采用随机特征映射，而是采用非迭代的方式从输入数据中获得植入有价值信息的有影响力的增强节点，从而提高了增强节点的质量，缓解了标准RVFL中随机分配参数带来的问题。由于网络参数是解析优化的，这种改进的变种可以保持标准RVFL的效率。此外，RVFL-PL被扩展为多层结构(mRVFL-PL)以从输入数据获得高级表示。在一些基准测试上的综合实验结果表明，与其他相应的方法相比，该方法的性能有所提高。
As a compact and effective learning model, the random vector functional link neural network (RVFL) has been confirmed with universal approximation capabilities. It has gained considerable attention in various fields. However, the randomly generated parameters in RVFL often lead to the loss of valid information and data redundancy, which severely degrades the model performance in practice. This paper first proposes an efficient network parameters learning approach for the original RVFL with pseudoinverse learner (RVFL-PL). Instead of taking the random feature mapping directly, RVFL-PL adopts a non-iterative manner to obtain influential enhancement nodes implanted with valuable information from input data, which realizes to improve the quality of the enhancement nodes and ease the problem caused by the randomly assigned parameters in the standard RVFL. Since the network parameters are optimized analytically, this improved variant can maintain the efficiency of the standard RVFL. Further, the RVFL-PL is extended to a multilayered structure (mRVFL-PL) to obtain high-level representations from the input data. The results of comprehensive experiments on some benchmarks indicate the performance improvement of the proposed method compared to other corresponding methods.