IEEE Trans Cybern. 2014 Apr;44(4):554-64. doi: 10.1109/TCYB.2013.2260537. Epub 2013 Jun 13.
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.
本文提出了一种具有自适应计算算法（SOFNN-ACA）的自组织模糊神经网络，用于建模一类非线性系统。该 SOFNN-ACA 通过同时进行结构和参数学习过程在线构建。在结构学习中，可以使用信息论方法自行设计一组模糊规则。具有高强度尖峰强度（SI）的模糊规则将被分为新规则。并且为了简化 FNN 结构，相对互信息（RMI）值较小的模糊规则将被修剪。在参数学习中，通过使用 ACA 来学习后件部分参数，该 ACA 将自适应学习率策略纳入学习过程以加快收敛速度。然后，分析 SOFNN-ACA 的收敛性。最后，将提出的 SOFNN-ACA 用于建模非线性系统。建模结果表明，该提出的 SOFNN-ACA 可以有效地对非线性系统进行建模。
