Open Access Open Access  Restricted Access Subscription or Fee Access

Fast Fuzzy Network Model

AmitaTelang ., Sunil Kumar Kashyap


Fuzzy is applied to design a network model for performing efficiently in this paper. The probability theory is applied to analyze the existed network model and the advantages transformed into the proposed fast fuzzy network model. Various analysis techniques and formulations are presented in this paper. The resultant is the fast fuzzy network with minimized error and maximized the security over the standard computational complexity.


Fuzzy; Network; Probability

Full Text:



Vetterlein, T. and Stˇepniˇcka, M.(2006), Completing fuzzy if-then rule bases ˇ by means of smoothing splines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14, 235–244. 187

Zadeh, L.A.(1973), Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. on Systems, Man and Cybernetics, 3, 28–44.

Zadeh, L.A.(1965), Fuzzy sets. Information and Control, 8, 338–353. [81] Zadeh, L.A.(1975), The concept of linguistic variable and its application to approximate reasoning I. Inform. Sci., 8, 199–250.

Zadeh, L.A.(1975), The concept of linguistic variable and its application to approximate reasoning II. Inform. Sci., 8, 301–357.

Zadeh, L.A.(1975), The concept of linguistic variable and its application to approximate reasoning III. Inform. Sci., 9, 43–80.

Zhang, Y., Wang, G. and Liu, S.(1998), Frequency domain methods for the solutions of N-order fuzzy differential equations. Fuzzy Sets and Systems, 94, 45–59.

Fayyad et al., (1996). The Primary Tasks of Data Mining, [www page]. URL

Dr. K. Lewnstein et. al., “Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test”, Medical and Biological Engineering and Computing , Volume 39, Issue 3, 2001, pp 362-367.

Victor-Emil Neagoe et. al., “A Neuro-Fuzzy Approach to Classification of ECG Signals for Ischemic Heart Disease Diagnosis”, AMIA AnnuSymp Proc. 2003, pp 494–498.

ConstantinosKoutsojannis and IoannisHatzilygeroudis, (2007). Using a Neurofuzzy Approach in Medical Application. In Springer-Verlag Berlin Heidelberg, 2007, pp. 477-484.

Li Shi et. al., Research on Diagnosing Coronary Heart Disease using Fuzzy Adaptive Resonance Theory Mapping Neural Networks, In Control and Automation, 2007. ICCA 2007, IEEE international Conference, Guangzhou, May 30 2007-June 1 2007, pp. 1126 – 1128.

NovruzAllahverdi et. al., “Design of a Fuzzy Expert System for Determination of Coronary Heart Disease Risk”, In International Conference on Computer Systems and Technologies – CompSysTech, 2007, pp. 14.1-14.7.

Tsipouras MG et. al., “Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling”, IEEE Transaction on Information Technology Biomedicine, 2008, Vol. 12 issue 4, pp 447- 458.

Markos G. Tsipouras et. al., “Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling”, IEEE Transactions on Information Technology in Biomedicine, Vol. 12 issue 4, 2008, pp. 447-456.

Harsh Vazirani," Use of Modular Neural Network for Heart Disease", Special Issue of IJCCT Vol.1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010 (pp 88-93).

Ali.Adeli, “A Fuzzy Expert System for Heart Disease Diagnosis", Proceedings of the international MultiConference of Engineers and computer scientists 2010 Vol 1, March 17-19, 2010, Hong Kong, ISSN 2078-0966.

Cite this Article

Deepshikha Sharma, Sunil Kumar Kashyap. The Formulation of Neural Network Model. Recent Trends in Electronics & Communication Systems. 2019; 6(2): 33–38p.


  • There are currently no refbacks.

Copyright (c) 2019 Recent Trends in Electronics and Communication Systems