In this study we are examining expert assessment (EA) non-linear scaled based approach to map original physical scale into a linguistical one. We introduce fuzzification of linguistic clustering mechanism for both input and output of a model under investigation, followed by appropriate decision-making apparatus. We also offered defuzzification approach for presented fuzzy model output. All results are thoroughly illustrated by proper experimental results.
References
[1]
Bezdek, J. (1981) C. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer.
[2]
Chiu, S.L. (1994) Fuzzy Model Identification Based on Cluster Estimation. JournalofIntelligent&FuzzySystems, 2, 267-278. https://doi.org/10.3233/ifs-1994-2306
[3]
Yager, R.R. and Filev, D.P. (1994) Generation of Fuzzy Rules by Mountain Clustering. JournalofIntelligent&FuzzySystems, 2, 209-219. https://doi.org/10.3233/ifs-1994-2301
[4]
Gustafson, D. and Kessel, W. (1978) Fuzzy Clustering with a Fuzzy Covariance Matrix. 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, San Diego, 10-12 January 1979, 761-766. https://doi.org/10.1109/cdc.1978.268028
[5]
Gath, I. and Geva, A.B. (1989) Unsupervised Optimal Fuzzy Clustering. IEEETransactionsonPatternAnalysisandMachineIntelligence, 11, 773-780. https://doi.org/10.1109/34.192473
[6]
Höppner, F. (1999) Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition. Wiley, 49-53.
[7]
Tserkovny, A. (2017) A T-Norm Fuzzy Logic for Approximate Reasoning. JournalofSoftwareEngineeringandApplications, 10, 639-662. https://doi.org/10.4236/jsea.2017.107035
[8]
Aliev, R. and Tserkovny, A. (2011) Systemic Approach to Fuzzy Logic Formalization for Approximate Reasoning. InformationSciences, 181, 1045-1059. https://doi.org/10.1016/j.ins.2010.11.021
[9]
Tserkovny, A. (2016) Fuzzy Logic for Incidence Geometry. TheScientificWorldJournal, 2016, Article ID: 9057263. https://doi.org/10.1155/2016/9057263
[10]
Tserkovny, A. (2017) A Fuzzy Logic Based Resolution Principal for Approximate Reasoning. JournalofSoftwareEngineeringandApplications, 10, 793-823. https://doi.org/10.4236/jsea.2017.1010045
[11]
Tserkovny, A. (2022) Some Considerations about Fuzzy Logic Based Decision Making by Autonomous Intelligent Actor. JournalofSoftwareEngineeringandApplications, 15, 19-58. https://doi.org/10.4236/jsea.2022.152002
[12]
Tserkovny, A. (2023) Dynamically Scaled Fuzzy Control of Autonomous Intelligent Actor. JournalofSoftwareEngineeringandApplications, 16, 301-313. https://doi.org/10.4236/jsea.2023.167016
[13]
Tserkovny, A. (2024) A Neuro T-Norm Fuzzy Logic Based System. JournalofSoftwareEngineeringandApplications, 17, 638-663. https://doi.org/10.4236/jsea.2024.178035
[14]
Fukami, S., Mizumoto, M. and Tanaka, K. (1980) Some Considerations on Fuzzy Conditional Inference. FuzzySetsandSystems, 4, 243-273. https://doi.org/10.1016/0165-0114(80)90014-7
[15]
Tserkovny, A. (1987) Non-Contradiction in Fuzzy Production Knowledge Bases. The Conference “Informatics-87”, Yerevan, October 1987, 192-194.
