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Trivedi, Jeegar Ashokkumar
- Neuro-fuzzy Advisory System for Banks with Type 2 Fuzzy Approach
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National Journal of System and Information Technology, Vol 4, No 1 (2011), Pagination: 62-68Abstract
India is rated second largest country in the world for its population. India still is a developing country and is progressing towards financial stability. Various different banks exists abide by the rule of Reserve Bank of India. To make India strengthen economically banks are issuing loans to people, institutes, industries and countries. Hence there is a need of an effective guiding system for banks that advice bank authorities how to give loans, what are different plans for loans are possible, what is the maximum revenue that can be earned from a business loan. The paper presents an effective advisory system for banks to take such crucial decision. The proposed system is developed with artificial neural networks and type 2 fuzzy logic. The structure of artificial neural network, training data, fuzzy membership functions used and implementation details are also discussed in this paper.Keywords
Artificial Neural Networks, Type 2 Fuzzy Logic, Neuro-fuzzy Systems, Bank Loan
References
- Abraham A, Adaptation of Fuzzy Inference System Using Neural Learning. http://www.softcomputing.net/nf_chapter.pdf, (Accessed 3rd January 2011).
- Castillo O, Melin P, (2008). Type 2 Fuzzy Logic: Theory and Application, Springer.
- JOHN, R.I., AND COUPLAND, S. (2007). Type-2 fuzzy logic: A historical view: IEEE Computational Intelligence Magazine, Vol.2, pp. 57-62.
- KARAKOSE M, AKIN E, (2004). Type 2 Fuzzy activation functions for multilayer feed forward neural network: IEEE International Conference on Systems, Man and Cybernetics.
- LEE CH, HONG JL, LIN YC, LAI WY (2003). TYPE-2 FUZZY NEURAL NETWORK SYSTEMS AND LEARNING: International Journal of Computational Cognition, Vol.1 (4), pp. 79–90.
- Liang Q, Mendel JM (2000). Interval Type-2 Fuzzy Logic Systems: Theory and Design: IEEE Trans. On Fuzzy Systems, Vol. 8 (5), pp. 535-550.
- Mendel JM (2007). Advances in Type-2 Fuzzy Sets and Systems: Information Sciences, vol. 177, pp. 84-110.
- Mendel JM (2007). Type-2 Fuzzy Sets and Systems: an Overview, IEEE Computational Intelligence Magazine, Vol. 2, pp. 20-29.
- Mendel JM (2008). Introduction to type-2 fuzzy sets and systems, multi-media educational course, sponsored by the IEEE Computational Intelligence Society. ISBN: 1-4244-1448-2.
- Rich E, Knight K, (2001). Artificial Intelligence. Tata McGraw Hill Publishing Co. Ltd. 21st Indian Reprint.
- S N Sivanandam SN, Deepa SN, (2007). Principles Of Soft Computing, Wiley, ISBN 10: 81-265-1075-7.
- Sajja PS (2008). Type-2 Fuzzy User Interface for Artificial Neural Network based Decision Support System for Course Selection: International Journal of Computing and ICT Research, Vol.2 (2), pp. 96-102.
- WU H., MENDEL JM. (2002). Uncertainty bounds and their use in the design of interval type-2 fuzzy logic system, IEEE Transactions on fuzzy systems, Vol. 10 (5), pp. 622-639.
- Zadeh LA (1975). The concept of a linguistic variable and its application to approximate reasoning: Informzition Sciences, vol. 8, pp. 43-80.