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Fuzzy Inference Network with Mamdani Fuzzy Inference System

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Computational Intelligence: Theories, Applications and Future Directions - Volume I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 798))

Abstract

In the modern era, the amount of data generated is increasing at an exponential rate. The generated data has both numeric as well as linguistic form. Learning or extracting relevant information from these types of data is a major challenge for researchers. In this chapter, we have proposed a generic architecture of a network built from Mamdani fuzzy inference system as its basic building blocks and it tries to learn the information from data. Each node of the network acts as a complete Mamdani fuzzy inference system mapping numeric as well as linguistic information of the data from input to output in terms of linguistic rule-based inference. Parameters of the input fuzzy membership functions appearing in the premise parts and output fuzzy membership functions appearing in consequent parts of the rules in the fuzzy rule base of each node in the network constitute overall parameters of the network. The proposed model is trained using advanced optimization techniques to optimize the network parameters for better performance. The effectiveness of the trained model is tested on two different datasets. The proposed approach is compared with the Takagi-Sugeno Fuzzy Inference Network and feed-forward artificial neural network with similar architecture.

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References

  1. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, pp. 649–666. Springer International Publishing (2016)

    Chapter  Google Scholar 

  2. Hwang, J., Zhou, Y.: Image Colorization with Deep Convolutional Neural Networks

    Google Scholar 

  3. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

  4. Cho, K., Van Merrinboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  5. Zhang, J., Zong, C.: Deep neural networks in machine translation: an overview. IEEE Intell. Syst. 30(5), 16–25 (2015)

    Article  Google Scholar 

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  7. Howard, A.G.: Some improvements on deep convolutional neural network based image classification. arXiv preprint arXiv:1312.5402 (2013)

  8. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)

    Google Scholar 

  9. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)

    Google Scholar 

  10. Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)

  11. Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  12. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  13. Sevakula, R.K., Verma, N.K.: Compounding general purpose membership functions for fuzzy support vector machine under noisy environment. IEEE Trans. Fuzzy Syst. 25(6), 1446–1459 (2017)

    Article  Google Scholar 

  14. Rajurkar, S., Singh, V., Verma, N.K., Cui, Y.: Deep stacked auto-encoder with deep fuzzy network for transcriptome based tumor type classification. BMC Bioinformatics, vol. 18 (2017)

    Google Scholar 

  15. Singh, D.J., Agrawal, P., Verma, N.K., Ghosh, A.K., Malagaudanavar, A.: Interval type-2 TS fuzzy model for angle of attack sensor of the aircraft. J. Intell. Fuzzy Syst., 1–11 (2017)

    Google Scholar 

  16. Singh, V., Dev, R., Dhar, N.K., Agrawal, P., Verma, N.K.: Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. IEEE Trans. Fuzzy Syst. (2018)

    Google Scholar 

  17. Lee, S.C., Lee, E.T.: Fuzzy neural networks. Math. Biosci. 23(1–2), 151–177 (1975)

    Article  MathSciNet  Google Scholar 

  18. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  19. Kwan, H.K., Cai, Y.: A fuzzy neural network and its application to pattern recognition. IEEE Trans. Fuzzy Syst. 2(3), 185–193 (1994)

    Article  Google Scholar 

  20. Deng, Y., Ren, Z., Kong, Y., Bao , F., Dai, Q.: A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans. Fuzzy Syst. (2016)

    Google Scholar 

  21. Zhou, S., Chen, Q., Wang, X.: Fuzzy deep belief networks for semi-supervised sentiment classification. Neurocomputing 131, 312–322 (2014)

    Article  Google Scholar 

  22. Casillas, J., Cordn, O., Del Jesus, M.J., Herrera, F.: Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans. Fuzzy Syst. 13(1), 13–29 (2005)

    Article  Google Scholar 

  23. Buckley, J.J., Hayashi, Y.: Fuzzy genetic algorithms for optimization. In: Proceedings of lnternational Joint Conference on Neural Networks, Nagoya, Japan, Vol. 1, pp. 725–728 (1993)

    Google Scholar 

  24. Buckley, J.J., Hayashi, Y.: Fuzzy genetic algorithm and applications, to appear in Fuzzy Sets and Systems

    Google Scholar 

  25. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  26. Verma, N.K., Rajurkar, S.: Developing deep fuzzy network with Takagi Sugeno fuzzy inference system. In: IEEE International Conference on Fuzzy Systems (2017) (FUZZ-IEEE 2017) (Presented)

    Google Scholar 

  27. Verma, N.K., Hanmandlu, M.: Adaptive non-additive generalized fuzzy systems. Applied Soft Comp. 10(3), 820–831, (2010)

    Article  Google Scholar 

  28. Verma, N.K., Haumandlu, M.: Data driven model using adaptive fuzzy system. Int. J. Autom. Control 2(4), 447–458 (2008)

    Article  Google Scholar 

  29. Verma, N.K., Hanmandlu, M.: Adaptability in additive fuzzy system via EM algorithm. Int. J. Art. Intell. Mach. Learn. (6), 35 (2006)

    Google Scholar 

  30. Broyden, C.G.: The convergence of a class of double-rank minimization algorithms. J. Inst. Math. Appl. 6, 76–90 (1970)

    Article  Google Scholar 

  31. Fletcher, R.: A new approach to variable metric algorithms. Comput. J. 13, 317–322 (1970)

    Article  Google Scholar 

  32. Goldfarb, D.: A family of variable metric updates derived by variational means. Math. Comput. 24, 23–26 (1970)

    Article  Google Scholar 

  33. Shanno, D.F.: Conditioning of quasi-newton methods for function minimization. Math. Comput. 24, 647–656 (1970)

    Article  MathSciNet  Google Scholar 

  34. Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83–93 (1988)

    Article  Google Scholar 

  35. Verma, N.K., Hanmandlu, M.: Additive and nonadditive fuzzy hidden Markov models. IEEE Trans. Fuzzy Syst. 18(1), 40–56 (2010)

    Article  Google Scholar 

  36. Verma, N.K., Hanmandlu, M.: From a gaussian mixture model to nonadditive fuzzy systems. IEEE Trans. Fuzzy Syst. 15(5), 809–827 (2007)

    Article  Google Scholar 

  37. Wang, L-X., Mendel, J.M.: Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: IEEE International Conference on Fuzzy Systems, San Diego, CA, pp. 1409–1418 (1992)

    Google Scholar 

  38. Jayaram, B., Mesiar, R.: On special fuzzy implications. Fuzzy Sets Syst. 160(14), 2063–2085 (2009)

    Article  MathSciNet  Google Scholar 

  39. Fletcher, R.: Practical Methods of Optimization, Vol. 1, and Unconstrained Optimization, Vol. 2, Constrained Optimization. Wiley (1980)

    Google Scholar 

  40. Richard, L., Douglas Faires, J., Reynolds, A.C.: Numerical Analysis. Prindle, Weber and Schmidt (1978)

    Google Scholar 

  41. Bonnans, J.-F., Gilbert, J.C., Lemarchal, C., Sagastizbal, C.A.: Numerical Optimization: Theoretical and Practical Aspects. Springer Science and Business Media (2013)

    Google Scholar 

  42. Wang, L.-X., Mendel, J.M.: Generating fuzzy rules from numerical data, with applications. University of Southern California, Department of Electrical Engineering-Systems, Signal and Image Processing Institute (1991)

    Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology, Kanpur, India

    Nishchal K. Verma, Vikas Singh, Shreedharkumar Rajurkar & Mohd Aqib

Authors
  1. Nishchal K. Verma
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  2. Vikas Singh
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  3. Shreedharkumar Rajurkar
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  4. Mohd Aqib
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Corresponding author

Correspondence to Vikas Singh .

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Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Verma, N.K., Singh, V., Rajurkar, S., Aqib, M. (2019). Fuzzy Inference Network with Mamdani Fuzzy Inference System. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_29

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