Journal articles: 'Similarity-based Reasoning'

1

Sun, Ron. "Robust reasoning: integrating rule-based and similarity-based reasoning." Artificial Intelligence 75, no. 2 (June 1995): 241–95. http://dx.doi.org/10.1016/0004-3702(94)00028-y.

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Mondal, Banibrata, and Swapan Raha. "Similarity-Based Inverse Approximate Reasoning." IEEE Transactions on Fuzzy Systems 19, no. 6 (December 2011): 1058–71. http://dx.doi.org/10.1109/tfuzz.2011.2159981.

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Biacino, Loredana, Giangiacomo Gerla, and Mingsheng Ying. "Approximate Reasoning Based on Similarity." MLQ 46, no. 1 (January 2000): 77–86. http://dx.doi.org/10.1002/(sici)1521-3870(200001)46:1<77::aid-malq77>3.0.co;2-x.

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Luo, Minxia, and Ruirui Zhao. "Fuzzy reasoning algorithms based on similarity." Journal of Intelligent & Fuzzy Systems 34, no. 1 (January 12, 2018): 213–19. http://dx.doi.org/10.3233/jifs-171140.

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Hüllermeier, Eyke. "Similarity-based inference as evidential reasoning." International Journal of Approximate Reasoning 26, no. 2 (February 2001): 67–100. http://dx.doi.org/10.1016/s0888-613x(00)00062-1.

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Raha, Swapan, Abul Hossain, and Sujata Ghosh. "Similarity based approximate reasoning: fuzzy control." Journal of Applied Logic 6, no. 1 (March 2008): 47–71. http://dx.doi.org/10.1016/j.jal.2007.01.001.

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Whitaker, Leslie A., Richard H. Stottler, and James A. King. "Case-Based Reasoning: Taming the Similarity Heuristic." Proceedings of the Human Factors Society Annual Meeting 34, no. 4 (October 1990): 312–15. http://dx.doi.org/10.1177/154193129003400416.

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Raha, S., N. R. Pal, and K. S. Ray. "Similarity-based approximate reasoning: methodology and application." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, no. 4 (July 2002): 541–47. http://dx.doi.org/10.1109/tsmca.2002.804787.

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Sessa, Maria I. "Approximate reasoning by similarity-based SLD resolution." Theoretical Computer Science 275, no. 1-2 (March 2002): 389–426. http://dx.doi.org/10.1016/s0304-3975(01)00188-8.

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Esteva, Francesc, Pere Garcia, Lluís Godo, and Ricardo Rodríguez. "A modal account of similarity-based reasoning." International Journal of Approximate Reasoning 16, no. 3-4 (April 1997): 235–60. http://dx.doi.org/10.1016/s0888-613x(96)00126-0.

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Loia, V., S. Senatore, and M. I. Sessa. "Mobile mail-agents through similarity-based reasoning." Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, no. 5 (August 1, 2002): 348–56. http://dx.doi.org/10.1007/s00500-002-0188-4.

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Ray, Kumar Sankar, and Mandrita Mondal. "Similarity-based fuzzy reasoning by DNA computing." International Journal of Bio-Inspired Computation 3, no. 2 (2011): 112. http://dx.doi.org/10.1504/ijbic.2011.039910.

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13

Finnie, Gavin, and Zhaohao Sun. "Similarity and metrics in case-based reasoning." International Journal of Intelligent Systems 17, no. 3 (February 11, 2002): 273–87. http://dx.doi.org/10.1002/int.10021.

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HE, BIN. "STUDY ON SIMILARITY AND SIMILARITY REASONING." International Journal of Information Technology & Decision Making 03, no. 02 (June 2004): 239–50. http://dx.doi.org/10.1142/s0219622004001082.

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Based on extension models of a new science called Extenics, this paper presents formalized study on similarity and similarity reasoning. The concepts of δ-similarity and δ-similar extension elements are introduced firstly, and then the substitution principles of similarity for solving incompatible problems are set up, showing that similarity substitution is an effective way to solve incompatible problems in the practical world. In addition, the quantitative calculation of similarity is discussed. Finally, three basic rules for similarity reasoning are developed as well.

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Li, Yingfang, Keyun Qin, Xingxing He, and Dan Meng. "Properties of Raha's similarity-based approximate reasoning method." Fuzzy Sets and Systems 294 (July 2016): 48–62. http://dx.doi.org/10.1016/j.fss.2015.05.008.

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Burkhard, H.-D. "Case completion and similarity in case-based reasoning." Computer Science and Information Systems 1, no. 2 (2004): 27–55. http://dx.doi.org/10.2298/csis0402027b.

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Case Completion investigates cases for complex problem solving tasks using Case-Based Reasoning. Such tasks consist of several steps, and related cases should support each intermediate decision. Related cases are of a constraint like style and need the handling of partial matching for retrieval. Related similarity measures are investigated, and the implementation by Case Retrieval Nets is proposed.

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Pȩski, Marcin. "Prior symmetry, similarity-based reasoning, and endogenous categorization." Journal of Economic Theory 146, no. 1 (January 2011): 111–40. http://dx.doi.org/10.1016/j.jet.2010.08.006.

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Nakamura, Kiyohiko, Andrew P. Sage, and Sosuke Iwai. "A similarity-based reasoning model for intelligent interfaces." Computers & Electrical Engineering 12, no. 3-4 (January 1986): 175–86. http://dx.doi.org/10.1016/0045-7906(86)90009-1.

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Lau, Adela, Eric Tsui, and W. B. Lee. "An ontology-based similarity measurement for problem-based case reasoning." Expert Systems with Applications 36, no. 3 (April 2009): 6574–79. http://dx.doi.org/10.1016/j.eswa.2008.07.033.

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Wahyudi, Eka, and Sri Hartati. "Case-Based Reasoning untuk Diagnosis Penyakit Jantung." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 11, no. 1 (January 31, 2017): 1. http://dx.doi.org/10.22146/ijccs.15523.

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Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR for diagnosing heart disease. The diagnosis process is done by inserting new cases containing symptoms into the system, then the similarity value calculation between cases uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold <0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%

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21

Cunningham, P. "A Taxonomy of Similarity Mechanisms for Case-Based Reasoning." IEEE Transactions on Knowledge and Data Engineering 21, no. 11 (November 2009): 1532–43. http://dx.doi.org/10.1109/tkde.2008.227.

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22

Turksen, I. B., and Z. Zhong. "An approximate analogical reasoning approach based on similarity measures." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 6 (1988): 1049–56. http://dx.doi.org/10.1109/21.23107.

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23

Liao, T. Warren, Zhiming Zhang, and Claude R. Mount. "Similarity measures for retrieval in case-based reasoning systems." Applied Artificial Intelligence 12, no. 4 (June 1998): 267–88. http://dx.doi.org/10.1080/088395198117730.

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Jayaram, Balasubramaniam. "Rule reduction for efficient inferencing in similarity based reasoning." International Journal of Approximate Reasoning 48, no. 1 (April 2008): 156–73. http://dx.doi.org/10.1016/j.ijar.2007.07.009.

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Perfilieva, Irina. "Closeness in similarity-based reasoning with an interpolation condition." Fuzzy Sets and Systems 292 (June 2016): 333–46. http://dx.doi.org/10.1016/j.fss.2015.03.013.

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26

Yeung, D. S., and E. C. C. Tsang. "A comparative study on similarity-based fuzzy reasoning methods." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 27, no. 2 (April 1997): 216–27. http://dx.doi.org/10.1109/3477.558802.

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27

Dongrui Wu and J. M. Mendel. "Perceptual Reasoning for Perceptual Computing: A Similarity-Based Approach." IEEE Transactions on Fuzzy Systems 17, no. 6 (December 2009): 1397–411. http://dx.doi.org/10.1109/tfuzz.2009.2032652.

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28

Own, Chung-Ming. "Handling partial truth on type-2 similarity-based reasoning." Expert Systems with Applications 36, no. 2 (March 2009): 3007–16. http://dx.doi.org/10.1016/j.eswa.2008.01.040.

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Luo, Minxia, Yajing Wang, and Ruirui Zhao. "Interval-valued fuzzy reasoning method based on similarity measure." Journal of Logical and Algebraic Methods in Programming 113 (June 2020): 100541. http://dx.doi.org/10.1016/j.jlamp.2020.100541.

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30

Wang, Shyue-Liang, Tzung-Pei Hong, and Wen-Yang Lin. "Answering Null Queries by Analogical Reasoning on Similarity-based Fuzzy Relational Databases." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 3 (May 20, 2001): 163–71. http://dx.doi.org/10.20965/jaciii.2001.p0163.

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We present here a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. Null queries are queries that elicit a null answer from a database. Analogical reasoning assumes that if two situations are known to be similar in some respects, it is likely that they will be similar in others. Application of analogical reasoning to infer approximate answers for null queries using fuzzy functional dependency and fuzzy equality relation on possibility-based fuzzy relational database has been studied. However, the problem of inferring approximate answers has not been fully explored on the similarity-based fuzzy relational data model. In this work, we introduce the concept of approximate dependency and define a similarity measure on the similaritybased fuzzy model, as extensions to the fuzzy functional dependency and fuzzy equality relation respectively. Under the framework of reasoning by analogy, our method provides a flexible query answering mechanism for null queries on the similarity-based fuzzy relational data model.

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31

Nurfalinda and Alena Uperati. "Case Based Reasoning untuk Diagnosis Penyakit Ikan Kakap Putih." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 9, no. 1 (May 31, 2020): 45–50. http://dx.doi.org/10.31629/sustainable.v9i1.1601.

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Case Based Reasoning (CBR) is one reasoning from an expert system, namely by reasoning from previous cases that have been stored on a case base to find out the solution of a new case. In case based reasoning there is a retrive process, in the retrieve process there is a similarity process, and to speed up the retrieve process it can use the indexing method. In this research will use the indexing method with Bayesian models and similarity processes using the nearest neighbor method. System testing techniques from this study with two testing techniques namely: the first testing technique using the Bayesian indexing model, the results of the indexing have produced white snapper disease, then proceed with similarity method with the nearest neighbor method used to determine the right solution from the previous case. has been saved on a case base. The second testing technique is without using indexing, the process is only by the nearest neighbor similarity method, the results of similarity in the form of disease and treatment solutions from previous cases that have been stored on a case base. System accuracy for testing with Bayesian model indexing and nearest neighbor similarity with threshold 0,70 is 86% and testing without indexing with Bayesian model with threshold 0,70 is 100%.

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32

Hoffmann, Maximilian, and Ralph Bergmann. "Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning." Algorithms 15, no. 2 (January 18, 2022): 27. http://dx.doi.org/10.3390/a15020027.

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Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.

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33

Chen, Jian Hua, Wei Hong Wang, and Jun Wen Li. "Case-Based Reasoning with GIS to Oil-Gas Reservoir Comprehensive Mapping." Advanced Materials Research 1073-1076 (December 2014): 2010–14. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.2010.

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Oil and natural gas are very important. As reservoir geological phenomena have characteristics of complexity and nonlinearity, this paper presented a new approach of case-based reasoning with GIS to oil-gas reservoir comprehensive mapping, to which the characteristics of geological phenomena and the spatial relations among them are much considered, the reasoning is based on the attribute similarity reasoning and the spatial similarity reasoning. Experiments to the east region adjacent to Sulige gas field in north China were carried out. The results show that this approach is effective and much efficient, and it may have certain universality to similar problems or phenomena.

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Stojkovic, Milos, Milan Trifunovic, Dragan Misic, and Miodrag Manic. "Towards analogy-based reasoning in semantic network." Computer Science and Information Systems 12, no. 3 (2015): 979–1008. http://dx.doi.org/10.2298/csis141103036s.

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In this paper an approach in realization of analogy-based reasoning in semantic networks is presented. New semantic model, called Active Semantic Model (ASM), was used. Core of the process is performed by ASM?s association (semantic relation) plexus upgrading procedure based on recognition and determining similarity between association plexuses. Determining similarity between association plexuses is performed by recognition of topological analogy between association plexuses. ASM responds to unpredicted input by upgrading new association plexus modeled on remainder of the context whose subset is recognized as topologically analogous association plexus.

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Wang, Haiqiao, and Ruikun Niu. "Knowledge Service Technology for Supporting Intelligent Product Design." Shock and Vibration 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/2561950.

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In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

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Plaza, Enric, Eva Armengol, and Santiago Ontañón. "The Explanatory Power of Symbolic Similarity in Case-Based Reasoning." Artificial Intelligence Review 24, no. 2 (October 2005): 145–61. http://dx.doi.org/10.1007/s10462-005-4608-6.

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37

Xiong, Ning. "Learning fuzzy rules for similarity assessment in case-based reasoning." Expert Systems with Applications 38, no. 9 (September 2011): 10780–86. http://dx.doi.org/10.1016/j.eswa.2011.01.151.

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38

Mougouie, Babak. "Integration of Similarity-based and Deductive Reasoning for Knowledge Management." KI - Künstliche Intelligenz 24, no. 2 (May 13, 2010): 169–73. http://dx.doi.org/10.1007/s13218-010-0019-1.

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39

Shu, Qiong, He Ping Chen, and Jin Guang Gu. "Semantic Reasoning-Based Chinese Recipe Recommender System." Advanced Materials Research 718-720 (July 2013): 1998–2004. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1998.

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For the blank of the recommender system for chinese recipes, this paper uses OWLS-WSDL to build a semantic reasoning-based chinese recipe recommender system. This system through the tool of Protégé to establish the ontology of chinese recipes and then add rules for ontology reasoning. On this basis bring out a catering algorithm, using Euclidean distance and Jaccard to calculate the similarity between the dishes. According to the similarity as well as user preference, provides a quick means of siding dishes for users.

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40

Sohn, Mye, Jun Hyeok Yim, Seongil Lee, and Hyun Jung Lee. "Ontology-based Dynamic and Semantic Similarity Calculation Method for Case-based Reasoning." Intelligent Automation & Soft Computing 20, no. 1 (January 2, 2014): 33–46. http://dx.doi.org/10.1080/10798587.2013.873303.

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41

Wahyudi, Eka, and Novi Indah Pradasari. "Case Based Reasoning untuk Diagnosis Penyakit Jantung Menggunakan Metode Minkowski Distance." INTECOMS: Journal of Information Technology and Computer Science 1, no. 1 (March 23, 2018): 119–23. http://dx.doi.org/10.31539/intecoms.v1i1.170.

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Case Based Reasoning is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR for diagnosing heart disease. The diagnosis process is done by inserting new cases containing symptoms into the system, then the similarity value calculation between cases uses the minkowski distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold less than 0.80, the case will be revised by experts. Revised successful cases are stored to add the system knowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using minskowski distance similarity correctly of 100 percent. Using minkowski get accuracy of 100 percent. Keywords : Case Based Reasoning, Minkowski Distance Similarity.

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42

RAY, KUMAR S., and MANDRITA MONDAL. "CLASSIFICATION OF SODAR DATA BY DNA COMPUTING." New Mathematics and Natural Computation 07, no. 03 (September 2011): 413–32. http://dx.doi.org/10.1142/s1793005711002074.

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In this paper, we propose a wet lab algorithm for classification of SODAR data by DNA computing. The concept of DNA computing is essentially exploited to generate the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity-based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity-based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to the existing forms of fuzzy reasoning by bringing it down to nanoscale. We exploit the concept of massive parallelism of DNA computing by designing this new classifier in the wet lab. This newly designed classifier is very much generalized in nature and apart from SODAR data, this methodology can be applied to other types of data also. To achieve our goal we first fuzzify the given SODAR data in a form of synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning. In the present approach, we can avoid the tedious choice of a suitable implication operator (for a particular operation) necessary for the classical approach to fuzzy reasoning based on fuzzy logic. We adopt the basic notion of DNA computing based on standard DNA operations. We consider double stranded DNA sequences, whereas, most of the existing models of DNA computation are based on single stranded DNA sequences. In the present model, we consider double stranded DNA sequences with a specific aim of measuring similarity between two DNA sequences. Such similarity measure is essential for designing the classifier in the wet lab. Note that, we have developed a completely new measure of similarity based on base pair difference which is absolutely different from the existing measure of similarity and which is very much suitable for expert system approach to classifier design, using DNA computing. In the present model of DNA computing, the end result of the wet lab algorithm produces multi valued status which can be linguistically interpreted to match the perception of an expert.

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Zhao, Yan Wei, F. Zhang, M. Y. Zhang, Jian Chen, and N. Su. "Extension Case-Based Reasoning for Product Configuration Design." Advanced Materials Research 69-70 (May 2009): 616–20. http://dx.doi.org/10.4028/www.scientific.net/amr.69-70.616.

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The interface was regarded as standard and not considered in traditional configuration design, which made it difficult to apply to the existence product configuration. The paper proposes an extension case-based reasoning for product configuration design. With matter-elements, reasoning model of Extension Case-Based Reasoning (ECBR) is established, and its corresponding algorithm is proposed. During the configuration design, the solution space of configuration schemes is obtained by the similarity calculation, and then the overall evaluation of similarity and compatible degrees is adopted to form the final configuration scheme. A prototype system of reducer configuration design is successfully developed according to the method, and it proves the proposed method that is feasible and effective.

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Wang, Hai Qiao, Bei Bei Sun, and Xian Fa Shen. "Case-Based Reasoning Rapid Design Approach for CNC Turret." Advanced Materials Research 819 (September 2013): 304–10. http://dx.doi.org/10.4028/www.scientific.net/amr.819.304.

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In order to provide more efficient knowledge services in the CNC turret design process, a rapid design method of a case-based reasoning is proposed. Firstly, according to different types of demand in case retrieval, the similarity measurement models for crisp and fuzzy attribute type demands are constructed respectively. Secondly, in the weights assignment, this paper utilized the deviation information of similarity values to calculate objective weights, and then combined the objective weights and subjective weights to form synthesis weights. Finally, the similarity measurement and weights coefficient assignment methods were applied in a CNC turret design CBR system, and using the calculation function of MATLAB. It was demonstrated that this method could improve the accuracy of case retrieval.

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Amriana, Amriana, Deny Wiria Nugraha, and Rahma Tanti. "Sistem Pakar Diagnosa Penyakit Lambung Menggunakan Metode Case Based Reasoning Berbasis Web." CESS (Journal of Computer Engineering, System and Science) 5, no. 1 (January 31, 2020): 114. http://dx.doi.org/10.24114/cess.v5i1.13596.

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ABSTRAKPenyakit lambung merupakan penyakit yang tidak bisa dianggap remeh, karena jika dibiarkan terus menerus dapat mengakibatkan penyakit yang lain muncul dan bisa juga menyebabkan kematian jika tidak segera ditangani. Penyakit lambung dapat disebabkan oleh pola makan yang tidak sesuai, beban pikiran dan juga infeksi yang disebabkan oleh bakteri. Dengan adanya permasalahan tersebut maka penelitian ini hanya dapat memberikan diagnosis penyakit lambung yaitu Gastritis, Dispepsia, dan Gerd. Gejala yang diberikan ke dalam sistem berdasarkan gejala dari dokter umum.Penelitian ini bertujuan untuk membuat sistem pakar diagnosa penyakit lambung menggunakan metode Case Based Reasoning (CBR), dimana dapat membantu para penderita lambung dalam mendiagnosa penyakit yang menyerang lambung, dengan menggunakan metode Case Based Reasoning (CBR). CBR dikembangkan dari sistem pembelajaran berbasis kesamaan (similarity) dan dengan perhitungan Certainty Factor.Berdasarkan pengujian dan analisis sistem pakar diagnosa penyakit lambung menggunakan metode Case Based Reasoning (CBR) berbasis Web, mampu mendiagnosis 3 penyakit lambung (Gastritis, Dispepsia, dan GERD) dengan perhitungan nilai kepastian menggunakan metode certainty factor dan perhitungan kemiripan dengan metode Case Based Reasoning (CBR). Kata Kunci : Sistem Pakar, Diagnosa penyakit lambung, Metode Case Based Reasoning, Similarity, Certainty Factor.

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Keke, Zhang, Luo Nianxue, and Li Yingbing. "STGA-CBR: A Case-Based Reasoning Method Based on Spatiotemporal Trajectory Similarity Assessment." IEEE Access 8 (2020): 22378–85. http://dx.doi.org/10.1109/access.2020.2970082.

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47

Zhang, Qian Sheng, Hai Xiang Yao, and Zhen Hua Zhang. "An Interval-Valued Fuzzy Reasoning Approach Based on Weighted Similarity Measure." Advanced Materials Research 143-144 (October 2010): 161–65. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.161.

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This paper presents a new approach for bidirectional interval-valued fuzzy reasoning by employing a weighted similarity measure between the fact and the antecedent portion of production rule, in which the vague terms appearing are represented by interval-valued fuzzy concepts. One numeric example is given to demonstrate the reasonability and flexibility of our proposed approach.

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48

Chun, Myung-Geun. "A similarity-based bidirectional approximate reasoning method for decision-making systems." Fuzzy Sets and Systems 117, no. 2 (January 2001): 269–78. http://dx.doi.org/10.1016/s0165-0114(99)00093-7.

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49

Gupta, Kalyan Moy, and Ali Reza Montazemi. "A connectionist approach for similarity assessment in case-based reasoning systems." Decision Support Systems 19, no. 4 (April 1997): 237–53. http://dx.doi.org/10.1016/s0167-9236(96)00063-2.

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50

Plaza, E., F. Esteva, P. Garcia, L. Godo, and R. López de Màntaras. "A logical approach to case-based reasoning using fuzzy similarity relations." Information Sciences 106, no. 1-2 (April 1998): 105–22. http://dx.doi.org/10.1016/s0020-0255(97)10008-1.

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Journal articles on the topic 'Similarity-based Reasoning'

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