Road profile input estimation using adaptive-neuro fuzzy inference system

Ye Chen Qin; Ji Fu Guan; Liang Gu; Yi Li; Rui Liu

doi:10.15918/j.tbit1001-0645.2015.05.009

Road profile input estimation using adaptive-neuro fuzzy inference system

Ye Chen Qin, Ji Fu Guan, Liang Gu^*, Yi Li, Rui Liu

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Based on adaptive neuro fuzzy inference system (ANFIS), a time domain estimation for road profile input was presented. Differential equations of quarter vehicle model were created, and white noise signal was employed to stimulate the model and the dynamic response was used to train the inverse dynamic model with ANFIS. In the simulation, different kinds of random excitations were used to verify the accuracy of ANFIS, and the effect of the number of membership function and combination of input data were also discussed. The result shows that ANFIS can be used for road estimation and its time domain reproduction.

Original language	English
Pages (from-to)	481-484 and 489
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	35
Issue number	5
DOIs	https://doi.org/10.15918/j.tbit1001-0645.2015.05.009
Publication status	Published - 1 May 2015

Keywords

Adaptive neuro fuzzy inference system (ANFIS)
Road estimation
Road profile
Time domain estimation

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10.15918/j.tbit1001-0645.2015.05.009

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title = "Road profile input estimation using adaptive-neuro fuzzy inference system",

abstract = "Based on adaptive neuro fuzzy inference system (ANFIS), a time domain estimation for road profile input was presented. Differential equations of quarter vehicle model were created, and white noise signal was employed to stimulate the model and the dynamic response was used to train the inverse dynamic model with ANFIS. In the simulation, different kinds of random excitations were used to verify the accuracy of ANFIS, and the effect of the number of membership function and combination of input data were also discussed. The result shows that ANFIS can be used for road estimation and its time domain reproduction.",

keywords = "Adaptive neuro fuzzy inference system (ANFIS), Road estimation, Road profile, Time domain estimation",

author = "Qin, {Ye Chen} and Guan, {Ji Fu} and Liang Gu and Yi Li and Rui Liu",

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T1 - Road profile input estimation using adaptive-neuro fuzzy inference system

AU - Qin, Ye Chen

AU - Guan, Ji Fu

AU - Gu, Liang

AU - Li, Yi

AU - Liu, Rui

PY - 2015/5/1

Y1 - 2015/5/1

N2 - Based on adaptive neuro fuzzy inference system (ANFIS), a time domain estimation for road profile input was presented. Differential equations of quarter vehicle model were created, and white noise signal was employed to stimulate the model and the dynamic response was used to train the inverse dynamic model with ANFIS. In the simulation, different kinds of random excitations were used to verify the accuracy of ANFIS, and the effect of the number of membership function and combination of input data were also discussed. The result shows that ANFIS can be used for road estimation and its time domain reproduction.

AB - Based on adaptive neuro fuzzy inference system (ANFIS), a time domain estimation for road profile input was presented. Differential equations of quarter vehicle model were created, and white noise signal was employed to stimulate the model and the dynamic response was used to train the inverse dynamic model with ANFIS. In the simulation, different kinds of random excitations were used to verify the accuracy of ANFIS, and the effect of the number of membership function and combination of input data were also discussed. The result shows that ANFIS can be used for road estimation and its time domain reproduction.

KW - Adaptive neuro fuzzy inference system (ANFIS)

KW - Road estimation

KW - Road profile

KW - Time domain estimation

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JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

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ER -

Road profile input estimation using adaptive-neuro fuzzy inference system

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Keywords

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