ZHANG W, FU L C, WU Z H,et al.Self-organizing fuzzy neural network based on attractor triple competitive swarm optimization （ATCSO） and neuron competition[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(3):10-20.

doi:10.16186/j.cnki.1673-9787.2024100001

Received:2025/02/12

Revised:2025/04/21

Published:2026/04/28

Self-organizing fuzzy neural network based on attractor triple competitive swarm optimization （ATCSO） and neuron competition

Zhang Wei1,2, Fu Liangchao1, Wu Zhonghua1,2

1.School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment， Henan Polytechnic University， Jiaozuo  454003， Henan， China

Abstract: Objectives To address the issues of poor interpretability in the structural adjustment of fuzzy neural networks （FNNs） and insufficient accuracy in parameter optimization， a self-organizing fuzzy neural network 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 index 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 improve 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 functions， 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 estimation results.  Conclusions The proposed NCSOFNN-ATCSO achieves a compact structure and higher prediction accuracy than existing network models.

Key words:fuzzy neural network；neuron competition；one-sided Jacobi method；triple competition mechanism；dynamic attractor