The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

1. Benuskova, L., Kasabov, N.: Computational Neuro-genetic Modelling. Springer, New York (2007)

   Book  Google Scholar 

2. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

   Book  MATH  Google Scholar 

3. Bezdek, J. (ed.): Analysis of Fuzzy Information, vol. 1, 2, 3. CRC Press, Boca Raton (1987)

   MATH  Google Scholar 

4. Bezdek, J.: A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition. Journal of Intelligent and Fuzzy Systems 1, 1–25 (1993)

   Google Scholar 

5. Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired Evolutionary Algorithm: A multi-model EDA. IEEE Transactions on Evolutionary Computation 13(6), 1218–1232 (2009)

   Article  Google Scholar 

6. Furuhashi, T., Hasegawa, T., Shin-ichi, H., Uchikawa, Y.: An Adaptive Fuzzy Controller Using Fuzzy Neural Networks. In: Proceedings of Fifth IFSA World Congress, pp. 769–772 (1993)

   Google Scholar 

7. Futschik, M.E., Kasabov, N.: Fuzzy Clustering in Gene Expression Data Analysis. In: Proceedings of the World Congress of Computational Intelligence WCCI 2002, Hawaii. IEEE Press (May 2002)

   Google Scholar 

8. Gerstner, W.: Time Structure of the Activity of Neural Network Models. Physical Review E 51, 738–758 (1995)

   Article  Google Scholar 

9. Hebb, D.: The Organization of Behavior. John Wiley and Sons, New York (1949)

   Google Scholar 

10. Hodgkin, A.L., Huxley, A.F.: A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve. Journal of Physiology 117, 500–544 (1952)

    Google Scholar 

11. Hopfield, J.J.: Pattern Recognition Computation Using Action Potential Timing for Stimulus Representation. Nature 376, 33–36 (1995)

    Article  Google Scholar 

12. Izhikevich, E.M.: Which Model to Use for Cortical Spiking Neurons? IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004)

    Article  Google Scholar 

13. Kasabov, N.: Incorporating Neural Networks into Production Systems and a Practical Approach Towards the Realisation of Fuzzy Expert Systems. Computer Science and Informatics 21(2), 26–34 (1991)

    Google Scholar 

14. Kasabov, N.: Hybrid Connectionist Production Systems. Journal of Systems Engineering 3(1), 15–21 (1993)

    Google Scholar 

15. Kasabov, N., Shishkov, S.: A Connectionist Production System With Partial Match and its Use for Approximate Reasoning. Connection Science 5(3-4), 275–305 (1993)

    Article  Google Scholar 

16. Kasabov, N.: Connectionist Fuzzy Production Systems. In: Ralesu, A.L. (ed.) Fuzzy Logic in Artifical Intelligence. LNCS (LNAI), vol. 847, pp. 114–128. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

17. Kasabov, N.: Hybrid Connectionist Fuzzy Production Systems – Towards Building Comprehensive AI. Intelligent Automation and Soft Computing 1(4), 351–360 (1995)

    Google Scholar 

18. Kasabov, N.: Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. MIT Press, Cambridge (1996)

    MATH  Google Scholar 

19. Kasabov, N., Kim, J.S., Michaeland, W., Gray, A.: FuNN/2 – A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition. Information Sciences - Applications 101(3-4), 155–175 (1997)

    Google Scholar 

20. Kasabov, N.: Evolving Fuzzy Neural Networks – Algorithms, Applications and Biological Motivation. In: Yamakawa, T., Matsumoto, G. (eds.) Methodologies for the Conception, Design and Application of Soft Computing, pp. 271–274. World Scientific (1998)

    Google Scholar 

21. Kasabov, N.: Evolving Fuzzy Neural Networks for on-line Supervised/unsupervised, Knowledge-based Learning. IEEE Transactions on Systems, Man, and Cybernetics 31(6), 902–918 (2001)

    Article  Google Scholar 

22. Kasabov, N.: Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines. Springer, Heidelberg (2002)

    MATH  Google Scholar 

23. Kasabov, N., Song, Q.: DENFIS: Dynamic, Evolving Neural-fuzzy Inference Systems and its Application for Time-series Prediction. IEEE Transactions on Fuzzy Systems 10, 144–154 (2002)

    Article  Google Scholar 

24. Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach, 1st edn. Springer, London (2007) (2003)

    MATH  Google Scholar 

25. Kasabov, N., Hu, Y.: Integrated Optimisation Method for Personalised Modelling and Case Study Applications. International Journal of Functional Informatics and Personalised Medicine 3(3), 236–256 (2010)

    Article  Google Scholar 

26. Soltic, S., Kasabov, N.: Knowledge Extraction From Evolving Spiking Neural Networks With Rank Order Population Coding. International Journal of Neural Systems 20(6), 437–445 (2010)

    Article  Google Scholar 

27. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian Learning Through Spike-timing-dependent Synaptic Plasticity. Nature Neuroscience 3, 919–926 (2000)

    Article  Google Scholar 

28. Song, Q., Kasabov, N.: TWNFI- A Transductive Neuro-fuzzy Inference System With Weighted Data Normalisation for Personalised Modelling. Neural Networks 19(10), 1591–1596 (2006)

    Article  MATH  Google Scholar 

29. Thorpe, S.J., Delorme, A., Van Rullen, R.: Spike-based Strategies for Rapid Processing. Neural Networks 14(6-7), 715–725 (2001)

    Article  Google Scholar 

30. Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An Experimental Unification of Reservoir Computing Methods. Neural Networks 20(3), 391–403 (2007)

    Article  MATH  Google Scholar 

31. Watts, M.J.: A Decade of Kasabov’s Evolving Connectionist Systems: A Review. IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews 39(3), 253–269 (2009)

    Article  Google Scholar 

32. Widiputra, H., Pears, R., Kasabov, N.: Multiple Time-Series Prediction Through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 161–172. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

33. Wysoski, S.G., Benuskova, L., Kasabov, N.: Evolving Spiking Neural Networks for Audiovisual Information Processing. Neural Networks 23(7), 819–835 (2010)

    Article  Google Scholar 

34. Yager Ronald, R., Filev, D.: Generation of Fuzzy Rules by Mountain Clustering. Journal of Intelligent and Fuzzy Systems 2, 209–219 (1994)

    Google Scholar 

35. Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A Neo Fuzzy Neuron and Its Application to System Identification and Prediction of the System Behaviour. In: Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka 1992, Iizuka, Japan, pp. 477–483 (1992)

    Google Scholar 

36. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

37. Zadeh, L.A.: A Theory of Approximate Reasoning. In: Hayes, J.E.M., Donald, L.I.M. (eds.) Machine Intelligence (based on the International Machine Intelligence Workshop), vol. 9, pp. 149–194. Elsevier, New York (1979)

    Google Scholar 

38. Zadeh, L.A.: Fuzzy Logic. IEEE Computer 21, 83–93 (1988)

    Article  Google Scholar 

Download references