河南理工大学学报（自然科学版）2026，Vol.45Issue(3)：10-20,11.DOI:10.16186/j.cnki.1673-9787.2024100001
基于ATCSO和神经元竞争的SOFNN设计
Self-organizing fuzzy neural network based on attractor triple competitive swarm optimization(ATCSO)and neuron competition
摘要
Abstract
Objectives To address the issues of poor interpretability in the structural adjustment of fuzzy neu-ral networks(FNNs)and insufficient accuracy in parameter optimization,a self-organizing fuzzy neural net-work based on neuron competition and attractor triple competitive swarm optimization(NCSOFNN-ATCSO)is proposed.Methods First,a neuron competition-based structural adjustment mechanism is developed to endow the network evolution process with biological interpretability.Second,a neuron competitiveness in-dex incorporating Axin2 gene expression level is constructed.Meanwhile,the output matrix of rule-layer neurons is decomposed using the one-sided Jacobi method to accurately quantify neuron importance and im-prove the precision of neuron competition.Finally,to enhance prediction accuracy,network parameters are optimized using a triple competitive swarm optimization algorithm with dynamic attractors(ATCSO).A triple competition mechanism is introduced to accelerate convergence,and dynamic attractors are designed to obtain superior parameter vectors.Results The performance of ATCSO is validated using benchmark func-tions,demonstrating superior efficiency and accuracy.In time series prediction tasks,the proposed NCSOFNN-ATCSO achieves higher accuracy and a more compact structure compared with other models.When applied to effluent ammonia nitrogen concentration prediction,the model provides accurate estima-tion results.Conclusions The proposed NCSOFNN-ATCSO achieves a compact structure and higher predic-tion accuracy than existing network models.关键词
模糊神经网络/神经元竞争/单边Jacobi/三重竞争机制/动态吸引子Key words
fuzzy neural network/neuron competition/one-sided Jacobi method/triple competition mecha-nism/dynamic attractor分类
信息技术与安全科学引用本文复制引用
张伟,付良超,吴中华..基于ATCSO和神经元竞争的SOFNN设计[J].河南理工大学学报（自然科学版）,2026,45(3):10-20,11.基金项目
国家自然科学基金资助项目(61903126) （61903126）
河南省科技攻关项目(222102210213) （222102210213）
