A class of hierarchical fuzzy systems with constraints on the fuzzy rules

Joo, Moon G.; Lee, J.S.

doi:10.1109/tfuzz.2004.840096

Cited by 75 publications

(49 citation statements)

References 22 publications

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“…In this equation, the composite rule base (N 11 + I 21 ) * N 12 for the SFS is derived in accordance with the topological expression in Eq. (26) and the associated merging operations for rule bases by means of Boolean matrices. The inputs x 1 , x 2 , x 3 are presented by five linguistic terms each, as shown in Figs.…”

Section: Rulesmentioning

confidence: 99%

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Linguistic composition based modelling by fuzzy networks with modular rule bases

Gegov

Arabikhan

Petrov

2015

Fuzzy Sets and Systems

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This paper proposes a linguistic composition based modelling approach by networked fuzzy systems that are known as fuzzy networks. The nodes in these networks are modules of fuzzy rule bases and the connections between these modules are the outputs from some rule bases that are fed as inputs to other rule bases. The proposed approach represents a fuzzy network as an equivalent fuzzy system by linguistic composition of the network nodes. In comparison to the known multiple rule base approaches, this networked rule base approach reflects adequately the structure of the modelled process in terms of interacting sub-processes and leads to more flexible solutions. The approach improves significantly the transparency of the associated model while ensuring a high level of accuracy that is comparable to the one achieved by established approaches. Another advantage of this fuzzy network approach is that it fits well within the existing approaches with single rule base and multiple rule bases.

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Section: Rulesmentioning

confidence: 99%

“…HFS is a special type of CFS that has a specific structure [23][24][25][26][27]. Each subsystem in HFS has two inputs and one output.…”

Section: Introductionmentioning

confidence: 99%

Linguistic composition based modelling by fuzzy networks with modular rule bases

Gegov

Arabikhan

Petrov

2015

Fuzzy Sets and Systems

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“…On the basis of the hierarchical design, the inference accuracy of FRDT is expected to improve (Joo and Lee, 2005;Delgado et al, 2002;Wang, 1999). Figure 4 shows that there are three inputs considered in the Level_I, including the TCPUU, the PCPUU, and TMU, by which the RT can be inferred.…”

Section: Hierarchical Structurementioning

confidence: 99%

“…Regarding the efficiency of hierarchical design, it has been discussed in several research articles. For example, Delgado et al proposed a multi-objective decision making scheme, allowing the evolutionary parameters based on a hierarchical genetic fuzzy system adjustable, so it not only improves the models performance (accuracy), but also guarantees the interpretability of the resulting fuzzy models (Delgado et al, 2002); Joo et al also demonstrated a scheme of hierarchical fuzzy system with constraints on the fuzzy rules approximating with high accuracy and fewer fuzzy rules (Joo and Lee, 2005); Wang proposed a hierarchical structure in a fuzzy system achieving good approximation accuracy (Wang, 1999). According to these works, it is therefore believed that improvement of inference accuracy can be achieved by use of the hierarchical design.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system

Ting

Chen

et al. 2008

Engineering Applications of Artificial Intelligence

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A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program.

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“…Interestingly, this subject has been carefully studied in the setting of fuzzy systems, cf. [2,7,[17][18][19][20] and this allows us to contrast this fundamental property in different architectural settings.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy polynomial neurons as neurofuzzy processing units

Park

Pedrycz

2006

Neural Comput & Applic

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In this study, we introduce and study fuzzy polynomial neurons (FPNs) being regarded as generic processing units in neurofuzzy computing. The underlying topology of FPNs is formed through fuzzy rules, fuzzy inference and polynomials. Each polynomial offers a nonlinear mapping and is centred around a modal value of the corresponding membership functions defined in the input space of the neuron. The adjustable order of the polynomial is essential when addressing the level of nonlinearity to be handled in the approximation problem. We demonstrate that fuzzy polynomial neurons form a certain class of functional neurons and afterwards discuss their properties and an overall design process. Furthermore, these neurons are discussed in the context of universal approximation and universal approximators

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A class of hierarchical fuzzy systems with constraints on the fuzzy rules

Cited by 75 publications

References 22 publications

Linguistic composition based modelling by fuzzy networks with modular rule bases

Linguistic composition based modelling by fuzzy networks with modular rule bases

A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system

Fuzzy polynomial neurons as neurofuzzy processing units

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