On Modelling of Data-Driven Monotone Zero-Order TSK Fuzzy Inference Systems using a System Identification Framework

Teh, Chin Ying; Kerk, Yi Wen; Tay, Kai Meng; Lim, Chee Peng

doi:10.1109/tfuzz.2018.2851258

Cited by 29 publications

(26 citation statements)

References 40 publications

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“…Models ( 13) structurally coincide with (14). These results confirm the fulfillment of the condition…”

Section: Definition 5 If the Framework Eysupporting

confidence: 79%

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Geometrical Framework Application Directions in Identification Systems. Review

Karabutov¹

2021

IJISA

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The approaches review of the framework application in identification problems is fulfilled. It is showed that this concept can have different interpretations of identification problems. In particular, the framework is understood as a frame, structure, system, platform, concept, and basis. Two directions of this concept application are allocated: 1) the framework integrating the number of methods, approaches or procedures; b) the mapping describing in the generalized view processes and properties in a system. We give the review of approaches that are the basis of the second direction. They are based on the analysis of virtual geometric structures. These mappings (frameworks) differ in the theory of chaos, accidents, and the qualitative theory of dynamic systems. Introduced mappings (frameworks) are not set a priori, and they are determined based of the experimental data processing. The main directions analysis of geometrical frameworks application is fulfilled in structural identification problems of systems. The review includes following directions: i) structural identification of nonlinear systems; ii) an estimation of Lyapunov exponents; iii) structural identifiability of nonlinear systems; iv) the system structure choice with lag variables; v) system attractor reconstruction.

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“…Models ( 13) structurally coincide with (14). These results confirm the fulfillment of the condition…”

Section: Definition 5 If the Framework Eysupporting

confidence: 79%

“…They have directed to the accounting of system different features that to give required quality to the identification system. Such the approach to the improvement of identification methods is dominating (see for example [11][12][13][14][15]).…”

Section: Related Workmentioning

confidence: 99%

Geometrical Framework Application Directions in Identification Systems. Review

Karabutov¹

2021

IJISA

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“…From the application perspective, the use of MFRR to other domains, e.g., decision making problems, education assessments, and risk assessment, will be evaluated. In addition, how to use the MFRR framework with (potentially noisy) monotone data samples [41] as additional information for FIS modeling purposes constitutes another interesting research direction. This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Discussionmentioning

confidence: 99%

A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology

et al. 2020

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A monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stage framework to develop a computationally efficient MFRR algorithm. The first stage determines the combinations of fuzzy rules to be relabeled by exploiting the prior information derived from a given non-monotone fuzzy rule base. This prior information includes the minimum number of fuzzy rules to be relabeled (denoted as k), as well as the states of fuzzy rules that must be, must not be, or may be relabeled. The second stage relabels the consequent parts of multiple sets of k noisy fuzzy rules obtained from the first stage, such that a monotone fuzzy rule base is produced. The third stage selects the most suitable relabeled fuzzy rule base among the potential monotone fuzzy rule bases obtained from the second stage, either objectively or subjectively. We provide insights into MFRR and discuss its practical implementation. In addition, a network flow method is fused with the proposed MFRR framework, resulting in an efficient computation scheme. The MFRR framework is applied to Failure Mode and Effect Analysis (FMEA) problems related to a sewage treatment plant and a public hospital. It is also evaluated with real FMEA information from a semiconductor plant. The results are analyzed and discussed, which positively demonstrate the effectiveness of the proposed MFRR framework in formulating a monotone TSK-FIS model for undertaking FMEA problems.

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“…We consider this issue to be a very important one because many data-driven design methods mainly focus on position of fuzzy sets, while fuzziness is relegated to a more marginal role. As an example, in some works SFPs are defined by looking at prototypes (often obtained after some optimization process), then fuzzy sets of triangular shape are defined so as to form a SFP [26,27,28,29]: in such a case, the information coming from prototypes actually settles the fuzzy sets position, while the determination of fuzziness is only functional to preserve the well-formedness of the partition.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO

Castiello¹,

Mencar²

2020

IJCIS

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We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points of fuzzy sets in a SFP and fix the position of the fuzzy sets in the Universe of Discourse. In this way, it is possible to isolate the parameters that characterize the fuzziness of the fuzzy sets, which are subject to fine-tuning through particle swarm optimization (PSO). In this paper, we propose a formulation of the parameter space that enables the exploration of all possible levels of fuzziness in a SFP. The experimental results show that the impact of fuzziness is strongly dependent on the defuzzification procedure used in fuzzy rule-based classifiers. Fuzziness has little influence in the case of winner-takes-all defuzzification, while it is more influential in weighted sum defuzzification, which however may pose some interpretation problems.

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On Modelling of Data-Driven Monotone Zero-Order TSK Fuzzy Inference Systems using a System Identification Framework

Cited by 29 publications

References 40 publications

Geometrical Framework Application Directions in Identification Systems. Review

Geometrical Framework Application Directions in Identification Systems. Review

A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology

Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO

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