Abstract
In the modern era of globalization and dynamism, organizations have to make intelligent but complex decisions. Computer applications are being used to aid in this complex decision-making process, but there is one big limitation to it. Computers lack fair judgment and expertise that a human expert possesses. In order to overcome this problem, soft computing techniques can be used. This paper discusses potential uses of a technique known as ‘Neurofuzzy Technology’ in improving decision-making. To establish a basis for conceptual understanding, the paper discusses Fuzzy Logic, Neural Networks and Neurofuzzy technologies in general, and then proposes how Neurofuzzy Technology can be used for decision-making. With these theoretical bases, a model is developed to demonstrate the usefulness of Neurofuzzy Technology in business decisions. This model is developed from a sample data file using Fuzzy Technology. The data is later used to train the system by using Neural Net technology. The trained data is evaluated to check the performance of the system in terms of accuracy of results obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Azvine, B., Azarmi, N., Tsui, K.C.: Soft computing - a tool for building intelligent systems. BT Technology Journal 14(4), 37–45 (1996)
von Altrock, C.: Fuzzy Logic and Neurofuzzy Applications in Business and Finance. Prentice Hall, NJ (1996)
von Altrock, C.: Applying fuzzy logic to business and finance, optimus, 2/2002, pp. 38–39 (2002)
von Altrock, C.: Practical Fuzzy-Logic Design - The Fuzzy-Logic Advantage. The Computer Applications Journal Circuit Cellar INKÂ 75 (1996)
Carpenter, G.A., Grossberg, S.: Fuzzy ARTMAP: A Synthesis of Neural Networks and Fuzzy Logic for Supervised Categorization and Nonstationary Prediction. In: Fuzzy Sets, Neural Networks and Soft Computing, Van Nostrand Reinhold (1994)
Juang, C.-F., Lin, C.T.: An online self-constructing neural fuzzy inference network and its applications. IEEE Transactions on Fuzzy Systems 6(1), 12–32 (1998)
Zlotkin, G., Rosenschein, J.S.: Mechanism design for automated negotiation, and its application to task-orientated domains. Artificial Intelligence, 86(2), 195–244 (1996)
FuzzyTECH User’s Manual and NeuroFuzzy Module 5.0, INFORM GmbH/Inform Software Corp (1997)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, Englewood Cliffs (1997)
Zadeh, L.A.: The Roles of Fuzzy Logic and soft computing in the conception, design and deployment of intelligent systems. In: Nwana, H.S., Azarmi, N. (eds.) Software agents and soft computing: concepts and applications, pp. 183–190. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abbasi, E., Abbasi, K. (2008). Enhancing Business Decisions with Neurofuzzy Technology. In: Hussain, D.M.A., Rajput, A.Q.K., Chowdhry, B.S., Gee, Q. (eds) Wireless Networks, Information Processing and Systems. IMTIC 2008. Communications in Computer and Information Science, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89853-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-89853-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89852-8
Online ISBN: 978-3-540-89853-5
eBook Packages: Computer ScienceComputer Science (R0)