Skip to main content
SpringerLink
Log in

The application of SOFNN based on PSO-ILM algorithm in nonlinear system modeling

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Fuzzy neural network (FNN) is the product of the combination of fuzzy theory and neural network. It combines the advantages of neural network and fuzzy theory, which has achieved great success in various fields. However, on the one hand, when the practical input is intricate and high dimensional data, the existing FNN can’t achieve a good modeling effect in the speed of convergence, the accuracy of modeling and generalization ability. On the other hand, the number of rules in FNN is fixed, which will also lead to the above problems in nonlinear system modeling. In this paper, a self-organizing fuzzy neural network based on Particle Swarm Optimization with improved Levenberg-Marquardt learning algorithm (SOFNN-PSO-ILM) is proposed for nonlinear system modeling. First, SOFNN based on PSO-ILM is built online by a method of constantly learning parameters and structures. In the process of structures learning, the number of fuzzy rules that have been set can be self-designed with the growing and pruning algorithm, which is based on the size of the singular value. In the process of parameters learning, PSO algorithm combined with ILM algorithm is used to update parameters. Then, the convergence and stability of SOFNN based on PSO-ILM are analyzed. Finally, the proposed method is used to model in the nonlinear system by three examples. The modeling results demonstrate that the proposed SOFNN based on PSO-ILM can model in nonlinear systems effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Li Y, Gault R, Mcginnity TM (2021) Probabilistic, recurrent, fuzzy neural network for processing noisy time-series data. IEEE Trans Neural Netw Learn Syst PP(99):1–10

    Google Scholar 

  2. Souza P, Lughofer E (2020) An advanced interpretable fuzzy neural network model based a uni-nullneuron constructed from n-uninorms. Fuzzy Sets Syst:1–26

  3. Lin CT, Lee CSG et al (1991) Neural-network-based fuzzy logic control and decision system. IEEE Trans Comput 40(12):1320–1336

    Article  MathSciNet  MATH  Google Scholar 

  4. Wei S, Lu J, He C et al (2021) An algorithm of fire situation information perception using fuzzy neural network. In: 2021 international wireless communications and mobile computing (IWCMC). IEEE, pp 1297–1302

  5. Deng Y, Ren Z, Kong Y et al (2016) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006–1012

    Article  Google Scholar 

  6. Zhang X, Feng J, Hong Z et al (2022) Modelling of the slope solute loss based on fuzzy neural network model. Comput Syst Sci Eng 42(2):677–688

    Article  Google Scholar 

  7. Sah B, Kumar P, Bose SK (2020) A fuzzy logic and artificial neural network-based intelligent controller for a vehicle-to-grid system. IEEE Syst J

  8. Wang Y, Wai RJ (2022) Adaptive fuzzy-neural-network power decoupling strategy for virtual synchronous generator in micro-grid. IEEE Trans Power Electron 37(4):3878–3891

    Article  Google Scholar 

  9. Li C, Li W, Ning J (2018) Calculation of ship collision risk index based on adaptive fuzzy neural network. In: Proceedings of the 2018 3rd international conference on modeling, simulation and applied mathematics (MSAM 2018), pp 223–227

  10. Yin H, Yi W, Wu J et al (2022) Adaptive fuzzy neural network pid algorithm for bldcm speed control system. Mathematics 10(1)

  11. Wu X, Huang Y (2022) Adaptive fractional-order non-singular terminal sliding mode control based on fuzzy wavelet neural networks for omnidirectional mobile robot manipulator. ISA Trans 121:258–267

    Article  Google Scholar 

  12. Li W, Liu K, Sun Z et al (2022) A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty*. Biomed Signal Process Control 71(A)

  13. El-Sousy FFM, Amin MM, Mohammed OA (2021) Robust optimal control of high-speed permanent-magnet synchronous motor drives via self-constructing fuzzy wavelet neural network. IEEE Trans Ind Appl 57 (1):999–1013

    Article  Google Scholar 

  14. Manikandan S, Nagaraj B (2022) Hyperparameter tuned bidirectional gated recurrent neural network for weather forecasting. Intell Autom Soft Comput 33(2):761–775

    Article  Google Scholar 

  15. Mansoori A, Effati S (2021) Parametric ncp-based recurrent neural network model: a new strategy to solve fuzzy nonconvex optimization problems. IEEE Trans Syst Man Cybern-Syst 51(4):2592–2601

    Article  Google Scholar 

  16. Tavoosi J, Zhang C, Mohammadzadeh A et al (2021) Medical image interpolation using recurrent type-2 fuzzy neural network. Front Neuroinform 15

  17. Kuo R, Cheng W, Lien W C et al (2019) Application of genetic algorithm-based intuitionistic fuzzy neural network to medical cost forecasting for acute hepatitis patients in emergency room. J Intell Fuzzy Syst 37(4):5455–5469

    Article  Google Scholar 

  18. Chen L (2020) A new thickness prediction method of atmospheric pollutants pm2.5 using improved pso-fnn combined with deep confidence network. Fresenius Environ Bull 29(8):6438–6445

    Google Scholar 

  19. Deng X, Xu T, Wang R (2018) Risk evaluation model of highway tunnel portal construction based on bp fuzzy neural network. Comput Intell Neurosci 2018:1–16

    Google Scholar 

  20. Khater AA, El-Nagar AM, El-Bardini M et al (2020) Online learning based on adaptive learning rate for a class of recurrent fuzzy neural network. Neural Comput Appl 32(12):8691–8710

    Article  Google Scholar 

  21. Precup RE, David RC, Petriu EM et al (2011) Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity. Soft Comput Ind Appl 96:141–150

    Google Scholar 

  22. Zhang Z, Chen SM (2021) Optimization-based group decision making using interval-valued intuitionistic fuzzy preference relations. Inf Sci 561:352–370

    Article  MathSciNet  Google Scholar 

  23. Zamfirache IA, Precup RE, Roman RC et al (2022) Reinforcement learning-based control using q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system. Inf Sci 583:99–120

    Article  Google Scholar 

  24. Cui W, Qu W, Jiang M et al (2021) The atmospheric model of neural networks based on the improved levenberg-marquardt algorithm. Open Astron 30(1):24–35

    Article  Google Scholar 

  25. Tao J, Yu Z, Zhang R et al (2021) Rbf neural network modeling approach using pca based lm-ga optimization for coke furnace system. Appl Soft Comput 111

  26. Wu Y, Guo Z, Huang Y (2021) High-precision and robust visible-light dynamic imaging positioning system based on improved kernelized correlation filters and fast-weighted levenberg-marquardt algorithm. Opt Eng 60(4)

  27. Chen L, Huang Y, Lu T et al (2022) Metering equipment running error estimation model based on genetic optimized lm algorithm. J Comput Methods Sci Eng 22(1):197–205

    Google Scholar 

  28. Hao J, Zhang G, Yang K et al (2022) Online unified solution for selective harmonic elimination based on stochastic configuration network and levenberg-marquardt algorithm. IEEE Trans Ind Electron 69 (10):10,724–10,734

    Article  Google Scholar 

  29. Nkandeu YK, Tiedeu A, Abanda Y et al (2022) Image encryption using the logistic map coupled to a self-synchronizing streaming. Multimed Tools Appl 81(12):17,131–17,154

    Article  Google Scholar 

  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. IEEE, pp 1942–1948

  31. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95 proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39–43

  32. Fregoso J, Gonzalez CI, Martinez GE (2021) Optimization of convolutional neural networks architectures using pso for sign language recognition. Axioms 10(3)

  33. Pandey A, Panwar VS, Hasan ME et al (2020) V-rep-based navigation of automated wheeled robot between obstacles using pso-tuned feedforward neural network. J Comput Design Eng 7(4):427–434

    Article  Google Scholar 

  34. Ji M, Liu P, Wu Q (2021) Feasibility of hybrid pso-ann model for identifying soybean diseases. Int J Cogn Inf Natural Intell 15(4)

  35. Song J, Zheng WX, Niu Y (2021) Self-triggered sliding mode control for networked pmsm speed regulation system: a pso-optimized super-twisting algorithm. IEEE Trans Ind Electron

  36. Li Y, Zhou L, Gao P et al (2022) Short-term power generation forecasting of a photovoltaic plant based on pso-bp and ga-bp neural networks. Front Energy Res 9

  37. Dai Y, Khandelwal M, Qiu Y et al (2022) A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting. Neural Comput Applic 34 (8):6273–6288

    Article  Google Scholar 

  38. Salimi-Badr A, Ebadzadeh MM (2022) A novel self-organizing fuzzy neural network to learn and mimic habitual sequential tasks. IEEE Trans Cybern 52(1):323–332

    Article  Google Scholar 

  39. Qiao J, He Z, Du S (2020) Prediction of pm2.5 concentration based on multi-source data and self-organizing fuzzy neural network. SN Appl Sci 2(4):1–17

    Article  Google Scholar 

  40. Zhou H, Zhang Y, Duan W et al (2020) Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme. Appl Soft Comput 95:106,516

    Article  Google Scholar 

  41. He H, Meng X, Tang J et al (2022) A novel self-organizing ts fuzzy neural network for furnace temperature prediction in mswi process. Neural Comput Applic:1–18

  42. Glass L, Mackey M (2010) Mackey-glass equation. Scholarpedia 5(3):6908

    Article  Google Scholar 

  43. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141

    Article  MathSciNet  MATH  Google Scholar 

  44. Shampine LF, Reichelt MW (1997) The matlab ode suite. SIAM J Sci Comput 18(1):1–22

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key Scientific Research Projects in Colleges and Universities of Henan Province(21A110025), the National Natural Science Foundation of China (Grant Nos. 62003378, 62103456, 61976237),the Science and Technology Innovation Team of Colleges and Universities in Henan Province (Grant No. 22IRTSTHN015),the Natural Science Foundation of Henan Province (Grant Nos. 202300410516, 212300410321, 202300410511), the Research Award Fund for Outstanding Young Teachers in Henan Provincial Institutions of Higher Education (Grant Nos. 2021GGJS111).

Author information

Authors and Affiliations

  1. School of Electronic and Information, Zhongyuan University of Technology, Zhongyuan District, Zhengzhou, 450007, Henan, China

    Huaijun Deng, Linna Liu, Jianyin Fang & Li Yan

Authors
  1. Huaijun Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linna Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianyin Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Linna Liu.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, H., Liu, L., Fang, J. et al. The application of SOFNN based on PSO-ILM algorithm in nonlinear system modeling. Appl Intell 53, 8927–8940 (2023). https://doi.org/10.1007/s10489-022-03879-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03879-5

Keywords

Search

Navigation