一种新的基于神经模糊推理网络的复杂系统模糊辨识方法

针对基于输入输出数据的复杂系统的模糊辨识问题,提出了一种新的神经模糊推理网络及相应的学习算法．学习算法被应用于系统的结构辨识与参数辨识．在结构辨识阶段,介绍了一种新的直接从输入输出数据中抽取和优化模糊规则的学习算法;在参数辨识阶段,提出和推导了一种非监督学习和监督学习相结合的混合式学习算法,实现模糊隶属函数的初步调整和优化．仿真结果表明,本文的方法可以同时满足对辨识精度、收敛速度、可读性和规则数的要求．

A New Fuzzy Identification Approach for Complex Systems Based on Neural-Fuzzy Inference Network

This paper proposes a novel neural-fuzzy inference network and learning algorithm for fuzzy identification of complex systems based on input-output data. The learning algorithm is used for both structure identification and parameter identification of the fuzzy model. In the process of structure identification, a new approach is introduced for rule extraction from input-output data directly. By combining both unsupervised and supervised learning, a hybrid learning algorithm is presented for initial adjustment and optimization of membership functions. Simulations illustrate good performance of the proposed network and learning algorithm in terms of accuracy, readability, number of rules and practicability.