Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach
Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach
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DOI：
10.1109/fuzz-ieee.2018.8491583
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Adrian Rubio Solis;Uriel Martinez-Hernandez;G. Panoutsos
中科院分区：
文献类型：
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作者：
Adrian Rubio Solis;Uriel Martinez-Hernandez;G. Panoutsos
对于前馈神经网络的参数识别，进化极限学习机（E-ELM）往往比传统的基于梯度的算法要高效得多。特别是，E-ELM通常更快，并且在准确性和模型简单性之间提供了更高的权衡。因此，本文证明了基于粒子群优化（PSO）和极限学习机（ELM）的E-ELM可以扩展到具有Karnik-Mendel类型约简层的区间型-2径向基函数神经网络（IT2-RBFNN）。为了评估E-ELM的效率，将IT2- rbfnn作为区间2型模糊逻辑系统（IT2 FLS）用于两种常用基准数据集的建模和混沌时间序列的预测。根据我们的研究结果，与其他现有的IT2模糊方法相比，应用于IT2- rbfnn的E-ELM不仅优于基于自适应梯度的算法，并且提供了更好的泛化，而且与纯模糊模型相似，IT2- rbfnn也能够保留一些模型解释和透明度。
Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for the parameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher trade-off between accuracy and model simplicity. For that reason, this paper shows that an E-ELM that is based on Particle Swarm Optimisation (PSO) and Extreme Learning machine (ELM) can be extended to the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) with a Karnik-Mendel type-reduction layer. To evaluate the efficiency of E-ELM, the IT2-RBFNN is used as an Interval Type-2 Fuzzy Logic System (IT2 FLS) for the modelling of two popular benchmark data sets and for the prediction of chaotic time series. According to our results, E-ELM applied to the IT2-RBFNN not only outperforms adaptive-gradient-based algorithms and provides a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to pure fuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency.