Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems
Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems
复制标题
DOI：
10.1109/tsmcb.2008.2010506
复制
发表时间：
2009-08-01
影响因子：
--
通讯作者：
Saratchandran, P.
中科院分区：
文献类型：
--
作者：
Rong, Hai-Jun;Huang, Guang-Bin;Saratchandran, P.
在此对应关系中，已经开发了在线顺序模糊的极限学习机（OS模糊elm）来进行功能近似和分类问题。首先显示了Takagi-Sugeno-Kang（TSK）模糊推理系统（FIS）的等效性与通用的单个隐藏层馈电网络，然后显示为开发OS-Fuzzy-Elm算法。这将导致FIS可以处理任何有界的非稳定分段连续成员资格功能。此外，可以使用具有固定或变化块大小的输入数据进行OS模糊性elm学习的学习。在OS模糊性ELM中，所有成员函数的前提参数首先随机分配，然后通过分析确定相应的结果参数。在非线性系统识别，回归和分类领域，使用现实世界中的基准问题对OS模糊性ELM与其他现有算法进行了性能比较。结果表明，提出的OS模糊性ELM产生相似或更好的精度，并且在训练时间至少降低了速度级。
In this correspondence, an online sequential fuzzy extreme learning machine (OS-Fuzzy-ELM) has been developed for function approximation and classification problems. The equivalence of a Takagi-Sugeno-Kang (TSK) fuzzy inference system (FIS) to a generalized single hidden-layer feedforward network is shown first, which is then used to develop the OS-Fuzzy-ELM algorithm. This results in a FIS that can handle any bounded nonconstant piecewise continuous membership function. Furthermore, the learning in OS-Fuzzy-ELM can be done with the input data coming in a one-by-one mode or a chunk-by-chunk (a block of data) mode with fixed or varying chunk size. In OS-Fuzzy-ELM, all the antecedent parameters of membership functions are randomly assigned first, and then, the corresponding consequent parameters are determined analytically. Performance comparisons of OS-Fuzzy-ELM with other existing algorithms are presented using real-world benchmark problems in the areas of nonlinear system identification, regression, and classification. The results show that the proposed OS-Fuzzy-ELM produces similar or better accuracies with at least an order-of-magnitude reduction in the training time.