非线性系统
计算机科学
模糊逻辑
非线性动力系统
模糊控制系统
随机过程
人工智能
数学优化
数学
统计
物理
量子力学
作者
Kang Li,Junfei Qiao,Dianhui Wang
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:32 (3): 948-957
被引量:12
标识
DOI:10.1109/tfuzz.2023.3315368
摘要
This article proposes a novel randomized neuro-fuzzy model called fuzzy stochastic configuration networks (F-SCNs), which integrates the Takagi–Sugeno (T–S) fuzzy inference system into SCNs to enhance its fuzzy inference capability. Unlike original SCNs, the hidden layer in SCNs is replaced by the T–S fuzzy inference module, which is responsible for fuzzifying the input data and performing fuzzy reasoning. The fuzzy rules generated by the fuzzy module are directly connected to the output layer of the network. In addition, an enhancement layer is added between the fuzzy module's output and the output layer of the network to extract nonlinear information contained in fuzzy rules. The parameters of fuzzy systems are determined by the distribution characteristics of the input-output data of the network, which enhances the interpretability of the model. Moreover, the parameters of the neuro-fuzzy model are learned by stochastic configuration algorithms. Therefore, the model inherits the fast learning speed and universal approximation capability of SCNs. A series of simulation experiments are carried out, including nonlinear dynamic system identification, sequence prediction, and benchmark data modeling from the real world to verify the feasibility and effectiveness of the proposed method. Finally, a soft sensing model for the effluent total phosphorus concentration in wastewater treatment processes is developed based on the proposed F-SCNs. The results show that the proposed method has good potential for nonlinear system modeling tasks compared to some classical neuro-fuzzy and nonfuzzy models.
科研通智能强力驱动
Strongly Powered by AbleSci AI