Airbag controller designed by adaptive-network-based fuzzy inference system (ANFIS)

Mon, Yi-Jen

doi:10.1016/j.fss.2007.06.005

Cited by 31 publications

(14 citation statements)

References 4 publications

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“…In particular, the most interesting variants cover the following topics: Transmission control including gear shifting algorithms and clutch control [122], Systems for diagnosing different vehicle systems and elements [123][124][125], Methods for the assessment of passive safety [126,127], Thermal and NVH comfort control systems [128,129], Vehicle body design [130], etc. Figure 6 shows the statistics of relevant journal publications.…”

Section: Fuzzy Methods For Miscellaneous Applicationsmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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Purpose The paper addresses evolution of fuzzy systems for core applications of automotive engineering. Methods The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. Results The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, humanmachine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. Conclusions In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.

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Section: Fuzzy Methods For Miscellaneous Applicationsmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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“…After this step, the model can control the problem [16]. ANFIS is an off-line learning model and it is completely used in modeling and control of dynamic systems by making a set of fuzzy if-then rules with suitable MFs [17,18]. Unlike our previous study of complex dynamic system "Chaotic Behavior of Soil Radon Gas and Applications", in this research we used ANFIS model which consist of five layers (Fig.…”

Section: Adaptive Network Based On Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Hazar Gölü’ndeki Radyoaktif Seviyelerin Belirlenmesi için Uyarlamalı Sinirsel-Bulanık Çıkarım Sistemi (ANFIS) ile Modellenmesi

Kamislioglu

Külahçı²

2018

Cumhuriyet Science Journal

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In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is proposed for the determination of alpha radioactivity of Hazar Lake waters and for the prediction of its unknown values. The model parameters of the lake water are pH, total hardness (TH), depth, electrical conductivity (EC), and alpha radioactivity. ANFIS model is performed using the back-propagation algorithm, which has the five layers. Average relative error between measurements predicted by theoretical (ANFIS) and experimental data is approximately 0.7043%. The relative error between the test data and the radioactivity data change between 0.06% and 14%. Additionally, validity of the model is also tested with a regression model. The predicted results with the ANFIS model is better as statistically than the regression model.

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“…This knowledge should permit to adjust some details in order to have the best performance of compaction with the minor cost of time and money. The procedure is particularly economical and versatile so that it could constitute a new tool for modifying, if necessary, only a few aspects during the compaction operations (Jang 1993;Guler, Ubeyli 2005;Mon 2007;Gu, Oyadiji 2008;Tahmasebi, Hezarkhani 2010).…”

Section: The Aim Of the Present Researchmentioning

confidence: 99%

Identification of the Most Important Factors in the Compaction Process

Amadore

Bosurgi

Pellegrino

2014

Journal of Civil Engineering and Management

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The compaction of a hot mix asphalt depends on a number of important factors whose control, during the execution of the pavement, is complex; however it is essential to achieve the results envisaged in the design with the least possible cost. Therefore, the evaluation of the effect of these variables on the material density is important as well as their quantification in real time. This research, starting from a survey of some environmental variables, together with the hot mix asphalt density using an electrical impedance device, through the application of a neuro-fuzzy technique, proposes a procedure to classify the most important features. These results can be rapidly deduced during the paving operations; calibrations required to correct the compaction can be applied on site without waiting further time necessary for the extraction of the cores and the subsequent laboratory analysis. In this way it is possible to identify with a better precision the aspects of the environmental context requiring more attention. In addition, the model permits the inclusion of new input variables and additional data that can be recorded in following phases.

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Airbag controller designed by adaptive-network-based fuzzy inference system (ANFIS)

Cited by 31 publications

References 4 publications

A review of fuzzy methods in automotive engineering applications

A review of fuzzy methods in automotive engineering applications

Hazar Gölü’ndeki Radyoaktif Seviyelerin Belirlenmesi için Uyarlamalı Sinirsel-Bulanık Çıkarım Sistemi (ANFIS) ile Modellenmesi

Identification of the Most Important Factors in the Compaction Process

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