The Experts below are selected from a list of 66690 Experts worldwide ranked by ideXlab platform
Hirosato Seki - One of the best experts on this subject based on the ideXlab platform.
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IUKM - On the Property of SIC Fuzzy Inference Model with Compatibility Functions
Lecture Notes in Computer Science, 2015Co-Authors: Hirosato SekiAbstract:The single input connected Fuzzy Inference model (SIC model) can decrease the number of Fuzzy rules drastically in comparison with the conventional Fuzzy Inference models. However, the Inference results obtained by the SIC model were generally simple comapred with the conventional Fuzzy Inference models. In this paper, we propose a SIC model with compatibility functions, which weights the rules of the SIC model. Moreover, this paper shows that the Inference results of the proposed model can be easily obtained even as the proposed model uses involved compatibility functions.
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On the monotonicity of Fuzzy Inference models
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2012Co-Authors: Hirosato SekiAbstract:Monotonicity property is very important in real systems. The monotonicity may need to be satisfied in a variety of application domains, e.g., control, medical diagnosis, educational evaluation, etc. A search in the literature reveals that the importance of the monotonicity in Fuzzy Inference system has been highlighted. Therefore, this paper surveys the works relating the monotonicity for various Fuzzy Inference systems. It firstly focuses on the monotonicity of the Mamdani Inference model. Themonotonicity ofMamdani model is shown by using a defuzzification method in cases of three t-norms. Secondly, the monotonicity conditions and applications of the T–S Inference model are stated. Finally, the monotonicity of the single input type Fuzzy Inference models is surveyed.
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On the Equivalence Conditions of Fuzzy Inference Methods—Part 1: Basic Concept and Definition
IEEE Transactions on Fuzzy Systems, 2011Co-Authors: Hirosato Seki, Masaharu MizumotoAbstract:This paper addresses equivalence of Fuzzy Inference methods. It first presents several well-known Fuzzy Inference methods: the product-sum-gravity method, the simplified Fuzzy Inference method, the Fuzzy singleton-type Inference method, the single input rule modules connected type Fuzzy Inference method (SIRMs method), and the single input connected Fuzzy Inference method (SIC method). Second, three Fuzzy Inference methods of the product-sum-gravity method, simplified Fuzzy Inference method, and Fuzzy singleton-type Inference method, which are all widely used as Fuzzy control methods, are shown to be equivalent to each other. Third, the equivalence conditions between the SIRMs method and the SIC method, known as single input type Fuzzy Inference method, are shown. Finally, it also gives the equivalence conditions between the single input type Fuzzy Inference methods and the previous three Fuzzy Inference methods. Investigating the equivalence among various Fuzzy Inference methods would help to understand the relationship of those Fuzzy Inference methods.
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SMC - On the equivalence of single input type Fuzzy Inference methods
2009 IEEE International Conference on Systems Man and Cybernetics, 2009Co-Authors: Hirosato Seki, Masaharu MizumotoAbstract:This paper addresses equivalence of Fuzzy Inference methods. It first presents single input type Fuzzy Inference methods: the single input rule modules connected type Fuzzy Inference method (SIRMs method) and single input connected Fuzzy Inference method (SIC method). Secondly, the equivalence conditions of the SIRMs method and SIC method are shown. Finally, this paper also discusses the equivalence conditions between the single input type Fuzzy Inference methods and the conventional Fuzzy Inference methods like the simplified Fuzzy Inference method, product-sum-gravity method and Fuzzy singleton-type Inference method which are all widely used as Fuzzy control methods.
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FUZZ-IEEE - On the equivalence of SIRMs connected Fuzzy Inference method
2009 IEEE International Conference on Fuzzy Systems, 2009Co-Authors: Hirosato SekiAbstract:This paper addresses equivalence of Fuzzy Inference methods. It first presents Fuzzy Inference methods: the product–sum–gravity method, Fuzzy singleton-type Inference method and single input rule modules connected type Fuzzy Inference method (SIRMs method). Secondly, three Fuzzy Inference methods of the product–sum–gravity method, Fuzzy singleton-type Inference method and SIRMs method which are all widely used as Fuzzy control methods are shown to be equivalent to each other. Finally, we propose a “Fuzzy singleton-type SIRMs method” as weighted SIRMs method, and also shown to be the equivalent between the proposed SIRMs method and above three Fuzzy Inference methods.
H. Mori - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Inference based identification of load characteristics
1996 IEEE International Symposium on Circuits and Systems (ISCAS), 1996Co-Authors: H. MoriAbstract:This paper proposes an efficient Fuzzy-logic based technique for identifying nodal load characteristics in power networks. Load dynamics is too complicated to understand the behavior. To capture the load characteristics, Fuzzy Inference is used to load modeling. As a Fuzzy technique, this paper makes use of the simplified Fuzzy Inference that handle the output variable as a crisp one. The proposed method is tested in field data.
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optimal Fuzzy Inference for short term load forecasting
IEEE Transactions on Power Systems, 1995Co-Authors: H. Mori, H KobayashiAbstract:This paper proposes an optimal Fuzzy Inference method for short-term load forecasting. The proposed method constructs an optimal structure of the simplified Fuzzy Inference that minimizes model errors and the number of the membership functions to grasp nonlinear behavior of power system short-term loads. The model is identified by simulated annealing and the steepest descent method. The proposed method is demonstrated in examples.
Masaharu Mizumoto - One of the best experts on this subject based on the ideXlab platform.
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On the Equivalence Conditions of Fuzzy Inference Methods—Part 1: Basic Concept and Definition
IEEE Transactions on Fuzzy Systems, 2011Co-Authors: Hirosato Seki, Masaharu MizumotoAbstract:This paper addresses equivalence of Fuzzy Inference methods. It first presents several well-known Fuzzy Inference methods: the product-sum-gravity method, the simplified Fuzzy Inference method, the Fuzzy singleton-type Inference method, the single input rule modules connected type Fuzzy Inference method (SIRMs method), and the single input connected Fuzzy Inference method (SIC method). Second, three Fuzzy Inference methods of the product-sum-gravity method, simplified Fuzzy Inference method, and Fuzzy singleton-type Inference method, which are all widely used as Fuzzy control methods, are shown to be equivalent to each other. Third, the equivalence conditions between the SIRMs method and the SIC method, known as single input type Fuzzy Inference method, are shown. Finally, it also gives the equivalence conditions between the single input type Fuzzy Inference methods and the previous three Fuzzy Inference methods. Investigating the equivalence among various Fuzzy Inference methods would help to understand the relationship of those Fuzzy Inference methods.
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SMC - On the equivalence of single input type Fuzzy Inference methods
2009 IEEE International Conference on Systems Man and Cybernetics, 2009Co-Authors: Hirosato Seki, Masaharu MizumotoAbstract:This paper addresses equivalence of Fuzzy Inference methods. It first presents single input type Fuzzy Inference methods: the single input rule modules connected type Fuzzy Inference method (SIRMs method) and single input connected Fuzzy Inference method (SIC method). Secondly, the equivalence conditions of the SIRMs method and SIC method are shown. Finally, this paper also discusses the equivalence conditions between the single input type Fuzzy Inference methods and the conventional Fuzzy Inference methods like the simplified Fuzzy Inference method, product-sum-gravity method and Fuzzy singleton-type Inference method which are all widely used as Fuzzy control methods.
Hiroaki Ishii - One of the best experts on this subject based on the ideXlab platform.
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On the Infimum and Supremum of Fuzzy Inference by Single Input Type Fuzzy Inference
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2009Co-Authors: Hirosato Seki, Hiroaki IshiiAbstract:Fuzzy Inference has played a significant role in many applications. Although the simplified Fuzzy Inference method is currently mostly used, the problem is that the number of Fuzzy rules becomes very huge and so the setup and adjustment of Fuzzy rules become difficult. On the other hand, Yubazaki et al. have proposed a “single input rule modules connected Fuzzy Inference method” (SIRMs method) whose final output is obtained by summarizing the product of the importance degrees and the Inference results from single input Fuzzy rule module. Seki et al. have shown that the simplified Fuzzy Inference method and the SIRMs method are equivalent when the sum of diagonal elements in rules of the simplified Fuzzy Inference method is equal to that of cross diagonal elements. This paper clarifies the conditions for the infimum and supremum of the Fuzzy Inference method using the single input type Fuzzy Inference method, from the view point of Fuzzy Inference.
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On the infimum and supremum of simplified Fuzzy Inference method
2008 IEEE Conference on Soft Computing in Industrial Applications, 2008Co-Authors: Hirosato Seki, Hiroaki IshiiAbstract:Fuzzy Inference has played significant role in many applications. Although simplified Fuzzy Inference method is currently mostly used, the problem is that the number of Fuzzy rules becomes very huge and so the setup and adjustment of Fuzzy rules become difficult. On the other hand, Yubazaki et al. have proposed ldquosingle input rule modules connected Fuzzy Inference methodrdquo (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the Inference results from single input Fuzzy rule module. Seki et al. have shown that simplified Fuzzy Inference method and SIRMs method are equal when the sum of diagonal elements in rules of simplified Fuzzy Inference method is equal to that of cross diagonal elements. However, the conditions of infimum and supremum between simplified Fuzzy Inference method and SIRMs method are not clarified. This paper clarifies the conditions for infimum and supremum of simplified Fuzzy Inference method using SIRMs method.
T. Arnould - One of the best experts on this subject based on the ideXlab platform.
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Deep combination of Fuzzy Inference and neural network in Fuzzy Inference software—FINEST
Fuzzy Sets and Systems, 1996Co-Authors: Shun'ichi Tano, T. Oyama, T. ArnouldAbstract:Abstract At the Laboratory for International Fuzzy Engineering Research in Japan (LIFE), we are now developing FINEST (Fuzzy Inference Environment Software with Tuning). The special features are (1) improved generalized modus ponens, (2) mechanism which can tune the Inference method as well as Fuzzy predicates and (3) software environment for debugging and tuning. In this paper, we give an outline of the software, and describe an important concept, a deep combination of the Fuzzy Inference and the neural network in FINEST, which enables FINEST to tune the Inference method itself. FINEST is now being used as a tool for quantification of the meaning of natural language sentences as well as a tool for Fuzzy modelling and Fuzzy control.
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FINEST: Fuzzy Inference environment software with tuning
Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on , 1995Co-Authors: T. Oyama, Shun'ichi Tano, T. Arnould, T. Miyoshi, Y. Kato, A. Bastian, M. UmanoAbstract:The Fuzzy computing project at the Laboratory for International Fuzzy Engineering Research (LIFE) has developed FINEST (Fuzzy Inference Environment Software with Tuning), a software environment for Fuzzy Inference with a mechanism to tune the Inference method as well as the Fuzzy predicates. We describe the special features of FINEST, an example of use, and the fields of application of the system. >
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A tuning method for Fuzzy Inference with Fuzzy input and Fuzzy output
Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 1994Co-Authors: T. Oyama, Shun'ichi Tano, T. ArnouldAbstract:Most studies on tuning of Fuzzy Inference are concerned with numerical inputs and outputs only, and very few research has been done on tuning of Fuzzy Inference with Fuzzy inputs and outputs. Moreover, in many cases the object of tuning are Fuzzy predicates only, apart from the other factors intervening in Fuzzy Inference. In this paper the authors propose a method to tune the Fuzzy Inference when inputs and outputs are given as Fuzzy sets. This method is similar to backpropagation and tunes the parameters of aggregation operators, implication functions and combination functions as well as the Fuzzy predicates which appear in the nodes of the network representing the calculation process of the Fuzzy Inference. Some results of tuning simulation are also shown. >
