Mamdani Model Based Adaptive Neural Fuzzy Inference System and its Application in Traffic Level of Service Evaluation

Chai, Yuan; Jia, Limin; Zhang, Zundong

doi:10.1109/fskd.2009.76

Cited by 51 publications

(34 citation statements)

References 9 publications

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“…Learning procedure presented by Yuanyuan Chai et al [8] was applied the Gradient Descent method for all model parameters modifications and all these parameters are nonlinear parameters. When there is adequate training data, we can achieve M-ANFIS model.…”

Section: Learning Proceduresmentioning

confidence: 99%

“…System modelling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with ill-defined and uncertain systems [1][2][3]5,8,9,12]. By contrast, a fuzzy inference system employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Training a Fuzzy Constraint Network

2017

IJSR

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Section: Learning Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Training a Fuzzy Constraint Network

2017

IJSR

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“…Mamdani model is used to design the system. It is based on fuzzy inference system which employs the fuzzy if-then rules and can model the human knowledge and reasoning process without employing precise quantitative analysis [8]. A two-stage system is proposed as shown in figure 2, and the system model is described in figure 3.…”

Section: Fuzzy Logic Modelingmentioning

confidence: 99%

Design of Dual Axis Solar Tracker System Based on Fuzzy Inference Systems

Hijawi¹,

Arafeh²

2016

IJSCAI

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This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling. Models are compared in terms of the correlation between the actual testing data output and their corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are usedto measure the models error size. In order to measure the effectiveness of the proposed models, we compare the output power produced by a fixed photovoltaic panels with the output which would be produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce 22% more power than a fixed panels system.

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“…The training patterns are evaluated by the ANFIS. The fact that Mamdani's Fuzzy Inference is intuitive, has widespread acceptance and well suited for human cognition, informed its choice as the inference mechanism (Chai, Jia, & Zhang, 2009). The architecture of the ANFIS is presented in Figure 4.…”

Section: Inference Subsystemmentioning

confidence: 99%

Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid

Ekong¹,

Inyang²,

Onibere³

2012

MAS

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Depression disorder is common in primary care, but its diagnosis is complex and controversial due to the conflicting, overlapping and confusing nature of the multitude of symptoms, hence the need to retain cases in a case base and reuse effective previous solutions for current cases. This paper proposes a neuro-fuzzy-Case Base Reasoning (CBR) driven decision support system that utilizes solutions to previous cases in assisting physicians in the diagnosis of depression disorder. The system represents depression disorder with 25 symptoms grouped into five categories. Fuzzy logic provided a means for handling imprecise symptoms. Local similarity between the input cases and retrieved cases was achieved using the absolute deviation as the distance metric, while adaptive neuro-fuzzy inference system handled fuzzy rules whose antecedents are the mapped local similarities of each category of symptoms for global similarity measurement, upon which the retrieved cases are ranked. The 5 best matched cases are subjected to the emotional filter of the system for diagnostic decision making. This approach derives strengths from the hybridization since the tools are complementary to one another.

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Mamdani Model Based Adaptive Neural Fuzzy Inference System and its Application in Traffic Level of Service Evaluation

Cited by 51 publications

References 9 publications

A Method for Training a Fuzzy Constraint Network

A Method for Training a Fuzzy Constraint Network

Design of Dual Axis Solar Tracker System Based on Fuzzy Inference Systems

Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid

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