Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an<i>n</i>-Ary Aggregation Function

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

doi:10.1109/tfuzz.2020.2986986

Cited by 16 publications

(11 citation statements)

References 47 publications

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“…Fuzzy set theory is also used in a fuzzy inference system (FIS) to generate a model between inputs (features in the case of fuzzy classification) and targets (classes in the case of fuzzy classification). Due to the use of FIS, such transition may need a set of fuzzy rules in which gathering a complete one is difficult (Jee, Tay, and Lim 2015;Kerk et al 2021). Previous researches have indicated all of the above concepts could adopt to the risk analysis due to the capability of fuzzy concept for modelling of uncertainty.…”

Section: The Aims and Innovations Of The Studymentioning

confidence: 99%

Applying a new systematic fuzzy FMEA technique for risk management in light steel frame systems

Yeganeh

Heravi

Razavian

et al. 2021

Journal of Asian Architecture and Building Engineering

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Light Steel Frame (LSF) system is mainly used for construction of short and intermediate-height buildings in developed countries whereas considerable heed is not given to it in developing countries. Unfamiliarity to LSF risks is one of the main reasons for this averseness so risk management can remedy this challenge and develop its application. Hence, this paper investigates the risk management of LSF considering design, construction and operation phase. Three steps entailing risk identification, assessment and responding using fuzzy Failure Mode and Effect Analysis (FMEA) technique are suggested for risk management implementation and validation of responses, a novel index with respect to weighted combination of project quality, time and cost is calculated. The methodology is demonstrated on a pilot study in a developing country. By using interview, 29 significant risks are extracted in design, construction and operation and then evaluated by proposed fuzzy method. Results showed that the share of the risks in these steps are 21%, 31% and 48% respectively. The results revealed that the risks in the construction and operation phases are higher than those in the design phase. The results also show that involving safety as a project object in the risk management process could eventuate acceptable results.

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Section: The Aims and Innovations Of The Studymentioning

confidence: 99%

Applying a new systematic fuzzy FMEA technique for risk management in light steel frame systems

Yeganeh

Heravi

Razavian

et al. 2021

Journal of Asian Architecture and Building Engineering

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show abstract

“…On learning fuzzy system from data, Kerk and Teh (2020) established the parametric conditions for the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) model to operate as an n-ary aggregation function via the specifications of fuzzy membership functions and fuzzy rules. They defined an ideal dataset for constructing n-TSK-FIS, characterized by the monotonicity, boundary, and consistency properties and provided a novel method for information fusion and unified semantic space [23]. Liu and Wang (2020) constructed a fuzzy aided detection system and combined it into correlation filter based algorithms.…”

Section: The Relevant Workmentioning

confidence: 99%

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

Zha

Liu

et al. 2021

IEEE Access

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A fuzzy radial basis inference network with grouped signal feature embedding (GFE-FINN) classification model is proposed for multi-source time-varying signal fusion analysis and feature knowledge embedding, which multi-channel signals are divided into several groups according to the sources, attribute, features and sensitivity of signals. Each pattern class of the grouped signal sample set is divided into several pattern subclasses which are more similar features according to the grouping index, and typical feature samples are extracted to implicitly express the category features knowledge of the grouped signal. A fuzzy radial basis process neuron (FRBPN) is defined, which is used as parametric membership functions, and the typical feature signal samples of the grouped pattern subclass are used as the kernel centers of FRBPN to realize the embedding of the diverse feature knowledge. Through the kernel transformation in FRBPN, the input signals of each group are fuzzified respectively. Fuzzy multiplication operation is used to realize the information synthesis based on the membership degree of grouped pattern subclasses and establish fuzzy reasoning and classification rules. The proposed method can realize the feature fusion based on fuzzy membership degree and the semantic representation based on fuzzy rules hierarchically. Through the learning of the sample set, fuzzy membership function, reasoning and classification rules are established adaptively. A comprehensive learning algorithm was given. An experiment was conducted using 4-groups 12-lead long ECG signals in diagnosis of difficult heart disease. The correct recognition rate reaches 87.95%, and the performance evaluation index and generalization ability are significantly improved. INDEX TERMSMulti-source time-varying signal classification, signal grouping, feature knowledge embedding, parametric membership function, fuzzy reasoning rules

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“…Some papers [8]- [19] discussed various useful mathematical conditions to satisfy the monotonicity property for different fuzzy inference systems' models; Some paper [21] discussed the data driven monotone fuzzy system. And the monotonicity is also discussed as an important property in aggregation functions too [20]. These works on fuzzy with monotone focus mainly on constructing an input and output model or introducing the fuzzy monotone function by using the fuzzy logic and language, not focus on the data analysis for feature selection or correlative analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Monotone Relationship Method With its Application on Inclusion Degree

Liang

Zhang

2022

IEEE Access

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This paper introduces a new fuzzy monotone relationship and its associated method, which are applied to feature selection and correlation analysis. Specifically, after the concept of a fuzzy monotone is introduced, this paper first defines a new fuzzy monotone relationship between inputs and output. Second, a fuzzy inclusive monotone model is constructed on inclusion degree through several proved propositions, together with presenting a fuzzy inclusive monotone decision membership function. Third, a new algorithm is developed according to the proposed model for feature selection or correlation analysis. Compared with several methods, the proposed algorithm has been validated on several data sets. The results indicate that the proposed algorithm is effective for the selection of numeric attributes, and the correlation analysis. The novel fuzzy monotone relationship and the method are validated through theoretic proof and experimental results.

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Parametric Conditions for a Monotone TSK Fuzzy Inference System to be ann-Ary Aggregation Function

Cited by 16 publications

References 47 publications

Applying a new systematic fuzzy FMEA technique for risk management in light steel frame systems

Applying a new systematic fuzzy FMEA technique for risk management in light steel frame systems

A Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding and Its Application in Multi-Source Signals Classification

A Novel Fuzzy Monotone Relationship Method With its Application on Inclusion Degree

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