Abstract
The world is inundated with data. For any definition of data, too, the amount generated per second is incredible. With the explosion of the Internet through the World Wide Web in the 1990s and early 2000s, as well as the more recent exponential explosion of the Internet of Things, it is without a doubt that making sense of this data is a primary research question of this century.
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
Saltz, J.S.: The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2066–2071. IEEE (2015)
Bhardwaj, A., Bhattacherjee, S., Chavan, A., Deshpande, A., Elmore, A.J., Madden, S., Parameswaran, A.G.: Datahub: Collaborative Data Science & Dataset Version Management at Scale (2014). arXiv preprint arXiv:1409.0798
Rollins, J.: Why we need a methodology for data science (2015). https://www.ibmbigdatahub.com/blog/why-we-need-methodology-data-science. Accessed 06 Mar 2019
Papadakis Ktistakis, I.: An autonomous intelligent robotic wheelchair to assist people in need: standing-up, turning-around and sitting-down. Doctoral dissertation, Wright State University (2018)
Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Trans. Syst. Man Cybern. 20(2), 419–435 (1990)
Abraham, A.: Adaptation of fuzzy inference system using neural learning. In: Fuzzy Systems Engineering, pp. 53–83. Springer, Berlin, Heidelberg (2005)
Davis, L.: Handbook of Genetic Algorithms (1991)
Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)
Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley (2005)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 1, 28–44 (1973)
Rao, J.B., Zakaria, A.: Improvement of the switching of behaviours using a fuzzy inference system for powered wheelchair controllers. In: Engineering Applications for New Materials and Technologies, pp. 205–217. Springer, Cham (2018)
Bourbakis, N., Ktistakis, I.P., Tsoukalas, L., Alamaniotis, M.: An autonomous intelligent wheelchair for assisting people at need in smart homes: a case study. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–7. IEEE (2015)
Ktistakis, I.P., Bourbakis, N.G.: Assistive intelligent robotic wheelchairs. IEEE Potentials 36(1), 10–13 (2017)
Ktistakis, I.P., Bourbakis, N.: An SPN modeling of the H-IRW getting-up task. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 766–771. IEEE (2016)
Ktistakis, I.P., Bourbakis, N.: A multimodal human-machine interaction scheme for an intelligent robotic nurse. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 749–756. IEEE (2018)
Mohamed, S. R., Shohaimay, F., Ramli, N., Ismail, N., Samsudin, S.S.: Academic poster evaluation by Mamdani-type fuzzy inference system. In: Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016), pp. 871–879. Springer, Singapore (2018)
Pourjavad, E., Mayorga, R.V.: A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. J. Intell. Manuf. 1–13 (2017)
Jain, V., Raheja, S.: Improving the prediction rate of diabetes using fuzzy expert system. IJ Inf. Technol. Comput. Sci. 10, 84–91 (2015)
Danisman, T., Bilasco, I.M., Martinet, J.: Boosting gender recognition performance with a fuzzy inference system. Expert Syst. Appl. 42(5), 2772–2784 (2015)
Thakur, S., Raw, S.N., Sharma, R.: Design of a fuzzy model for thalassemia disease diagnosis: using Mamdani type fuzzy inference system (FIS). Int. J. Pharm. Pharm. Sci. 8(4), 356–61 (2016)
Genetic Algorithm. https://en.wikipedia.org/wiki/Genetic_algorithm. Accessed 24 Mar 2019
Gong, M., Yang, Y.H.: Multi-resolution stereo matching using genetic algorithm. In: Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), pp. 21–29. IEEE (2001)
Brown, C., Barnum, P., Costello, D., Ferguson, G., Hu, B., Van Wie, M.: Quake ii as a robotic and multi-agent platform. Robot. Vis. Tech. Rep. [Digital Repository] (2004). Available at HTTP. http://hdl.handle.net/1802/1042.
Yasuda, G.I., Takai, H.: Sensor-based path planning and intelligent steering control of nonholonomic mobile robots. In: IECON’01 27th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 317–322 (Cat. No. 37243). IEEE (2001)
Sandstrom, K., Norstrom, C.: Managing complex temporal requirements in real-time control systems. In: Proceedings Ninth Annual IEEE International Conference and Workshop on the Engineering of Computer-Based Systems, pp. 103–109. IEEE (2002)
Uz, M.E., Hadi, M.N.: Optimal design of semi active control for adjacent buildings connected by MR damper based on integrated fuzzy logic and multi-objective genetic algorithm. Eng. Struct. 69, 135–148 (2014)
Bobillo, F., Straccia, U.: The fuzzy ontology reasoner fuzzyDL. Knowl.-Based Syst. 95, 12–34 (2016)
Di Noia, T., Mongiello, M., Nocera, F., Straccia, U.: A fuzzy ontology-based approach for tool-supported decision making in architectural design. Knowl. Inf. Syst. 1–30 (2018)
Groth, W3C.: PROV-O: The PROV Ontology. https://www.w3.org/TR/prov-o/. Accessed 6 Apr 2019
Shimizu, C., Hitzler, P., Paul, C.: Ontology design patterns for Winston’s taxonomy of part-whole-relationships. Proceedings WOP (2018).
Straccia, U.: Fuzzy semantic web languages and beyond. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 3–8. Springer, Cham (2017)
Straccia, U.: An Introduction to Fuzzy & Annotated Semantic Web Languages (2018). arXiv preprint arXiv:1811.05724
Straccia, U.: A minimal deductive system for general fuzzy RDF. In: International Conference on Web Reasoning and Rule Systems, pp. 166–181. Springer, Berlin, Heidelberg (2009)
Straccia, U.: Towards a fuzzy description logic for the semantic web (preliminary report). In: European Semantic Web Conference, pp. 167–181. Springer, Berlin, Heidelberg (2005)
Pan, J.Z., Stamou, G., Tzouvaras, V., Horrocks, I.: f-SWRL: a fuzzy extension of SWRL. In: International Conference on Artificial Neural Networks, pp. 829–834. Springer, Berlin, Heidelberg (2005)
Lopes, N., Polleres, A., Straccia, U., Zimmermann, A.: AnQL: SPARQLing up annotated RDFS. In: International Semantic Web Conference, pp. 518–533. Springer, Berlin, Heidelberg (2010)
Nguyen, V.T.K.: Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs (2017)
Bonatti, P.A., Decker, S., Polleres, A., Presutti, V.: Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2019)
Hould, J.N.: Craft Beers Dataset, Version 1. https://www.kaggle.com/nickhould/craft-cans. Accessed 10 Mar 2019 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ktistakis, I.P., Goodman, G., Shimizu, C. (2021). Methods for Optimizing Fuzzy Inference Systems. In: Phillips-Wren, G., Esposito, A., Jain, L.C. (eds) Advances in Data Science: Methodologies and Applications. Intelligent Systems Reference Library, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-030-51870-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-51870-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51869-1
Online ISBN: 978-3-030-51870-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)