Skip to main content
SpringerLink
Log in

An improved approach of self-organising fuzzy neural network based on similarity measures

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

This paper proposes a self-learning approach to automatically generate an accurate and compact fuzzy neural network. The proposed algorithm is an integrated approach including addition, pruning and merging strategies. The merging strategy is based on a similarity measure of membership functions. Such an integrated approach has the advantages of reducing model overfitting, enhancing interpretability and improving computing effectiveness. Benchmark examples are presented for comparative analysis of model accuracy and compactness.

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
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  • Angelov P (2010) Evolving Takagi-Sugeno fuzzy systems from streaming data (eTS+). In: Angelov P, Filev DP, Kasabov N (eds) Evolving intelligent systems: methodology and applications. John Wiley & Sons, New Jersey

  • Angelov PP, Filev DP (2004) An approach to online identification of takagi-sugeno fuzzy models. IEEE Trans Sys Man Cybern Part B Cybern 34(1):484–498

    Article  MATH  Google Scholar 

  • Angelov PP, Kordon A (2010) Adaptive inferential sensors based on evolving fuzzy models: an industrial case study. IEEE Trans Syst Men Cybern Part B Cybern 40(2):529–539

    Article  Google Scholar 

  • Angelov P, Zhou X (2008) On line learning fuzzy rule-based system structure from data streams. In: Proceedings of 2008 IEEE International Conference on Fuzzy Systems within the IEEE World Congress on Computational Intelligence, IEEE Computational Intelligent Society, Hong Kong, pp 915–922

  • Astrom KJ, Wittenmark B (1995) Adaptive control. Addison-Wesley, Boston

  • Billings SA, Voon WSF (1986) Correlation based model validity tests for nonlinear models. Int J Control 44:235–244

    Article  MATH  Google Scholar 

  • Billings SA, Zhu Q (1994) Non-linear model validation using correlation tests. Int J Control 60:1107–1120

    Article  MathSciNet  MATH  Google Scholar 

  • Bollena J, Maoa H, Zeng X-J (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

    Article  Google Scholar 

  • Carpenter G et al (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5):698–713

    Article  Google Scholar 

  • Chao CT, Chen YJ, Teng CC (1996) Simplification of fuzzy-neural systems using similarity analysis. IEEE Trans Syst Man Cybern Part B Cybern 26(2):344–354

    Article  Google Scholar 

  • Chen MY, Linkens DA (2004) Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets Syst 142(2):243–265

    Article  MathSciNet  MATH  Google Scholar 

  • Cho KB, Wang BH (1996) Radial basis function based adaptive fuzzy systems and their applications to identification and prediction. Fuzzy Sets Syst 83:325–339

    Article  MathSciNet  Google Scholar 

  • Cun YL, Denker JS, Solla SA (1990) Optimal brain damage. In: Advances in neural information processing systems, vol 2. Morgan Kaufmann, San Mateo, pp 598–605

  • José de Jesús Rubio (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Google Scholar 

  • Delgado MR, Von Zuben F, Gomide F (2003) Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy. In: Casillas J, Cordón O, Herrera F, Magdalena L (eds) Interpretability issues in fuzzy modeling. Springer, Berlin Heidelberg, pp 379–405

    Google Scholar 

  • Er MJ, Liu F, Li MB (2010) Self-constructing fuzzy neural networks with extended Kalman filter. Int J Fuzzy Syst 12(1):66–72

    Google Scholar 

  • Gabrys B, Bargiela A (2000) General fuzzy min-max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769–783

    Article  Google Scholar 

  • Hassibi B and Stork DG (1993) Second order derivatives for network pruning: optimal brain surgeon. In: Advances in neural information processing systems, vol 5. Morgan Kaufmann, San Mateo, pp 164–171

  • Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley, Boston

  • Hong X, Billings SA (1997) Givens rotation based fast backward elimination algorithm for RBF neural network pruning. IEE Proc Control Theory Appl 144(5):381–384

    Article  MATH  Google Scholar 

  • Jang JSR (1993) Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–684

    Article  MathSciNet  Google Scholar 

  • Jin Y, Seelen WV, Sendhoff B (1999) On generating fc3 fuzzy rule systems from data using evolution strategies. IEEE Trans Syst Man Cybern Part B Cybern 29(6):829–845

    Article  Google Scholar 

  • Juang C-F, Hsieh C–D (2010) A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling. IEEE Trans Fuzzy Syst 18(2):261–273

    Google Scholar 

  • Kadirkamanathan V, Niranjan M (1993) A function estimation approach to sequential learning with neural networks. Neural Comput 5:954–975

    Article  Google Scholar 

  • Kasabov NK (2001) On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks. Neurocomputing 41:25–45

    Article  MATH  Google Scholar 

  • Kasabov NK, Song Q (2002) Denfis: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154

    Article  Google Scholar 

  • Lee CC (1990) Fuzzy logic in control systems: Fuzzy logic controller—part I and II. IEEE Trans Syst Man Cybern 20(2):404–435

    Article  MATH  Google Scholar 

  • Leng G, McGinnity TM, Prasad G (2005) An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst 150(2):211–243

    Article  MathSciNet  MATH  Google Scholar 

  • Leng G et al (2009) A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems. Appl Soft Comput 9(4):1354–1366

    Article  MathSciNet  Google Scholar 

  • Lughofer E, Angelov P (2011) Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl Soft Comput 11(2):2057–2068

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  • Mao H, Zeng X-J, Leng G, Zhai Y, Keane JA (2009) Short-term and midterm load forecasting using a bilevel optimization model. IEEE Trans Power Syst 24(2):1080–1090

    Article  Google Scholar 

  • Ouyang CS, Lee WJ, Lee SJ (2005) A TSK-type neurofuzzy network approach to system modeling problems. IEEE Trans Sys Man Cybern Part B Cybern 35(4):751–767

    Article  Google Scholar 

  • Rojas I, Pomares H, Bernier JL, Ortega J, Pino B, Pelayo FJ, Prieto A (2002) Time series analysis using normalized pg-rbf network with regression weights. Neurocomputing 42(1–4):267–285

    Article  MATH  Google Scholar 

  • Rong HJ, Sundararajan N, Huang GB, Saratchandran P (2006) Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260–1275

    Article  MathSciNet  MATH  Google Scholar 

  • Simpson P (1992) Fuzzy min-max neural networks. I. Classification. IEEE Trans Neural Netw 3(5):776–786

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    MATH  Google Scholar 

  • Tanaka K, Sugeno M (1992) Stability analysis and design of fuzzy control systems. Fuzzy Sets Syst 45:135–156

    Article  MathSciNet  MATH  Google Scholar 

  • Tolstrup N (1995) Pruning of a large network by optimal brain damage and surgeon: an example from biological sequence analysis. Int J Neural Syst 6:31–42

    Article  Google Scholar 

  • Wan F, Shang H, Wang LX, Sun YX (2005) How to determine the minimum number of fuzzy rules to achieve given accuracy: a computational geometric approach to siso case. Fuzzy Sets Syst 150(2):199–209

    Article  MathSciNet  MATH  Google Scholar 

  • Wang WJ (1997) New similarity measures on fuzzy sets and on elements. Fuzzy Sets Syst 85:305–309

    Article  MATH  Google Scholar 

  • Wang N, Er MJ, Meng X (2009) A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks. Neurocomputing 72(16–18):3818–3829

    Article  Google Scholar 

  • Wang D, Zeng X-J, Keane JA (2010) An evolving-construction scheme for fuzzy systems. IEEE Trans Fuzzy Syst 18(4):755–770

    Article  Google Scholar 

  • Wu S, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594

    Article  Google Scholar 

  • Zhang J, Morris AJ (1995) Fuzzy neural networks for nonlinear systems modelling. IEE Proc Control Theory Appl 142(6):551–561

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Manchester, Manchester, M13 9PL, UK

    Gang Leng, Xiao-Jun Zeng & John A. Keane

Authors
  1. Gang Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Jun Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John A. Keane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Leng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Leng, G., Zeng, XJ. & Keane, J.A. An improved approach of self-organising fuzzy neural network based on similarity measures. Evolving Systems 3, 19–30 (2012). https://doi.org/10.1007/s12530-012-9045-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-012-9045-6

Keywords

Search

Navigation