Abstract
Computing huge amounts of information and performing complex operations in a unique fuzzy logic system is a challenge in the field of fuzzy logic. This paper presents a Knowledge engineering application whereby a Fuzzy Network (FN) is used to build a complex computing model to reproduce corporate dynamics and to implement a Model Reference Adaptive Control (MARC) strategy for Corporate Control [2]. This model is used as a What If? Environment to explore future consequences of actions planned within a strategic scenario context in terms of KPIs displayed in a Balanced ScoreCard (BSC) control board. Corporation’s strategy map is required to plan the Knowledge Identification and Capture Activity (KICA) required to obtain the knowledge to be represented in the FN’s nodes rule bases. KICA produces linguistic variables as well as the qualitative relationships amongst them. A FN appears as a natural solution to model the knowledge distributed within the members participating in all analysis and decision making tasks along the organization. Additionally, as proof of concept a prototype which capable of designing and simulating networks of fuzzy systems is presented based on the standard IEC 61131-7.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Laskri, M.T., Beggas, M., Médini, L., Laforest, F.: Towards an ideal service QoS in fuzzy logic-based adaptation planning middleware. J. Syst. Softw. 92, 71–81 (2014)
Zadeh, L.A.: Fuzzy logic: issues, contentions and perspectives. In: 1994 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1994, vol. 6, p. VI/183 (1994)
Sarkar, A.: Application of fuzzy logic in transport planning. Int. J. Soft Comput. 3(2), 1 (2012)
Hoyos, G.P.: Pipeline risk assessment using a fuzzy systems network. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1495–1498 (2013)
Seising, R., Trillas, E., Kacprzyk, J. (eds.): Towards the Future of Fuzzy Logic, vol. 325. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18750-1
Duarte, O.G., Pérez, G.: Unfuzzy: fuzzy logic system analysis, design, simulation and implementation software. In: Proceedings of the EUSFLAT-ESTYLF Joint Conference, Palma de Mallorca, Spain, 22–25 September 1999, pp. 251–254 (1999)
Norton, D.P., Kaplan, R.S.: Transforming the Balanced Scorecard from Performance Measurement to Strategic Management, 15th edn. Harvard Business School Publishing Corporation, Boston (2001)
Wang, L.-X.: Adaptive Fuzzy Systems and Control. PTR Prentice Hall, Upper Saddle River (1994)
Norton, D.P., Kaplan, R.S.: Strategy Maps, Converting Intangible Assets into Tangible Outcomes. Harvard Business School Publishing Corporation (2004)
Gustavo, P.: A fuzzy logic based expert system for short term energy negotiations. In: 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No. 99TH8397), pp. 149–152 (1999)
Commission, International Electrotechnical technical committee industrial process measurement and control. Programmable Controllers, Part 7 - Fuzzy Control Programming. Commission, International Electrotechnical technical committee industrial process measurement and control (1997)
Cingolani, P., Alcalá-Fdez, J.: jFuzzyLogic: a Java library to design fuzzy logic controllers according to the standard for fuzzy control programming. Int. J. Comput. Intell. Syst. 6(Suppl. 1), 61–75 (2013)
Zadeh, L.A.: Is there a need for fuzzy logic? Inf. Sci. 178(13), 2751–2779 (2008)
Alfaro-Garcia, V.G., Gil-Lafuente, A.M., Klimova, A.: A fuzzy approach to competitive clusters using moore families. In: Rutkowski, L., et al. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9119, pp. 137–148. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19324-3_13
Camastra, F., Ciaramella, A., Giovannelli, V., Lener, M., Rastelli, V., Staiano, A., Staiano, G., Starace, A.: A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Syst. Appl. 42(3), 1710–1716 (2015)
Meschino, G.J., Nabte, M., Gesualdo, S., Monjeau, A., Passoni, L.I.: Fuzzy tree studio: a tool for the design of the scorecard for the management of protected areas. In: Espin, R., Pérez, R.B., Cobo, A., Marx, J., Valdés, A.R. (eds.) Soft Computing for Business Intelligence. SCI, vol. 537, pp. 99–112. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-53737-0_6
Yaakob, A.M., Gegov, A., Rahman, S.F.A.: Decision making problem solving using fuzzy networks with rule base aggregation. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2017)
Cruz-Vega, I., Garcia-Limon, M., Escalante, H.J.: Adaptive-surrogate based on a neuro-fuzzy network and granular computing. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 761–768 (2014)
Nápoles, G., Mosquera, C., Falcon, R., Grau, I., Bello, R., Vanhoof, K.: Fuzzy-rough cognitive networks. Neural Netw. 97, 19–27 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Velandia, J., Pérez, G., Bolivar, H. (2018). Fuzzy Networks Model, a Reliable Adoption in Corporations. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-96133-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96132-3
Online ISBN: 978-3-319-96133-0
eBook Packages: Computer ScienceComputer Science (R0)