INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.8, No.6, Jun. 2016

Estimation and Approximation Using Neuro-Fuzzy Systems

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Author(s)

Nidhi Arora, Jatinderkumar R. Saini

Index Terms

Soft Computing;Neuro-Fuzzy System;Estimation and Approximation;Decision-making; Uncertainty;Non-linearity

Abstract

Estimation and Approximation plays an important role in planning for future. People especially the business leaders, who understand the significance of estimation, practice it very often. The act of estimation or approximation involves analyzing historical data pertaining to domain, current trends and expectations of people connected to it. Exercising estimation is not only complicated due to technological change in the world around, but also due to complexity of the problems. Traditional numerical based techniques for solution of ill-defined non-linear real world problems are not sufficient. Hence, there is a need of some robust methodologies which can deal with dynamic environment, imprecise facts and uncertainty in the available data to achieve practical applicability at low cost. Soft computing seeks to solve class of problems not suited for traditional algorithmic approaches.
To address the common problems in business of inexactness, some models are put forward for servicing, support and monitoring by approximating and estimating important outcomes. This work illustrates some very general yet widespread problems which are of interest to common people. The suggested approaches can overcome the fuzziness in traditional methods by predicting some future events and getting better control on business. This includes study of various neuro-fuzzy architectures and their possible applications in various areas, where decision-making using classical methods fail.

Cite This Paper

Nidhi Arora, Jatinderkumar R. Saini,"Estimation and Approximation Using Neuro-Fuzzy Systems", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.6, pp.9-18, 2016. DOI: 10.5815/ijisa.2016.06.02

Reference

[1]Anandarajan, M., Anandarajan, A., & Srinivasan, C. A. (2003). Business Intelligence Techniques: A Perspective from Accounting and Finance. Springer.

[2]Arora, N. & Saini, J.R. (2014). Study of Existing Work on Soft Computing Methodologies and Fusion of Neural Network and Fuzzy Logic for Estimation and Approximation. International Journal for Research in Applied Science and Engineering Technology, 2(5), 1-5.

[3]Arora, Nidhi and Vij, SanjayK. (2013). Reckoner for health risk and insurance premium using adaptive neuro-fuzzy inference system, Neural Computing and Applications, 23(7-8), 2121-2128.

[4]Ataei, S., Aghakouchak, A. A., Marefat, M. S., & Mohammadzadeh, S. (2005). Sensor fusion of a railway bridge load test using neural networks. Expert Systems with Applications, 29(3), 678–683.

[5]Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. Neural Networks, IEEE Transactions On, 12(4), 929–935.

[6]Biswas, R. (1995). An application of fuzzy sets in students’ evaluation. Fuzzy Sets and Systems, 74(2), 187–194.

[7]Chen, S.-M., & Lee, C.-H. (1999). New methods for students’ evaluation using fuzzy sets. Fuzzy Sets and Systems, 104(2), 209–218.

[8]Deniz, D. Z., & Ersan, I. (2002). An Academic Decision Support System Based on Academic Performance Evaluation for Student and Program Assessment. International Journal of Engineering Education, 18(2), 236–244.

[9]Dilijonas, D., Kriksciunien, D., Sakalauskas, V., & Simutis, R. (2009). Sustainability based service quality approach for automated teller machine network. Accessed, 16(02), 2012.

[10]Fortuna, L. (2001). Soft Computing: New Trends and Applications. Springer.

[11]Graupe, D. (2007). Principles of artificial neural networks. Vol. 6. World Scientific.

[12]Ham, F. M., & Kostanic, I. Principles of neurocomputing for science and engineering, 2001. McGraw-Hill.

[13]Jo, H., & Han, I. (1996). Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert Systems with Applications, 11(4), 415–422.

[14]Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169(2), 677–697.

[15]Ma, J., & Zhou, D. (2000). Fuzzy set approach to the assessment of student-centered learning. Education, IEEE Transactions On, 43(2), 237–241.

[16]McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.

[17]Ramirez, C., & Acuña, G. (2011). Forecasting cash demand in ATM using neural networks and least square support vector machine. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 515–522.

[18]Simutis, R., Dilijonas, D., & Bastina, L. (2008). Cash demand forecasting for ATM using neural networks and support vector regression algorithms. In 20th EURO Mini Conference, Vilnius, 416–421.

[19]Shin, K.S. & Lee, Y. J. (2002), A genetic algorithm application in bankruptcy prediction modeling, Expert Systems with Applications, 23(3), 321-328.

[20]Shin, K.S., Lee, T. S., & Kim, H. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135.

[21]Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7), 926–947.

[22]Teddy, S. D., & Ng, S. K. (2011). Forecasting ATM cash demands using a local learning model of cerebellar associative memory network. International Journal of Forecasting, 27(3), 760–776.

[23]Wu, J.-D., Hsu, C.-C., & Chen, H.-C. (2009). An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(4), 7809–7817.

[24]Wang, Y.-M., & Elhag, T. (2008). An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications, 34(4), 3099–3106.

[25]Zadeh, Lotfi A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

[26]Zanganeh, T., Rabiee, M., & Zarei, M. (2011). Applying Adaptive Neuro-Fuzzy Model for Bankruptcy Prediction. International Journal of Computer Applications, 20.