Particle filtering approach to membership function adjustment in fuzzy logic systems

Chung, Jun Ho; Pak, Jung Min; Ahn, Choon Ki; You, Sung Hyun; Lim, Myo Taeg; Song, Moon Kyou

doi:10.1016/j.neucom.2016.10.006

Cited by 21 publications

(7 citation statements)

References 35 publications

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“…In this study, the selected membership function and the number of fuzzy subsets were manually adjusted by trial-and-error method. Increasing the number of a fuzzy subset in the membership function would give precise results (Abdulshahed et al, 2015;Chung et al, 2017). The applied trial-and-error method resulted that three fuzzy subsets of membership function are assigned in the GRC of R a , F c and MRR, while nine fuzzy subsets of membership function were set for GFRG as shown in Figure 6.…”

Section: Results Of Experimentsmentioning

confidence: 99%

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Soepangkat

Norcahyo

Pramujati

et al. 2019

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Purpose The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42. Design/methodology/approach The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (Fc) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L18 mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method. Findings The optimum SR, cutting force (Fc) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics. Originality/value To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

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Section: Results Of Experimentsmentioning

confidence: 99%

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Soepangkat

Norcahyo

Pramujati

et al. 2019

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“…It generates some initial random samples and starts to compute the membership function's parameters recursively which is more robust against nonlinearity compared with the Extended Kalman Filter Membership Function Adjustment (EKFMFA) [9]. W. Liao introduce a procedure for Generating Interval Type-2 Membership Functions from Data which called extended This procedure has been shown to cover triangular, trapezoidal, s, z, and interval type-2 membership functions as special cases [21].…”

Section: Literature Reviewsmentioning

confidence: 99%

Fuzzifying Telecommunication Network Features Basing PSO-TE Algorithm

Elsanabary¹,

Gamal²,

El-Fetouh³

et al. 2017

IJSEIA

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Planning Telecommunication Access Network (TAN) infrastructure is a real time problem that suffers from uncertainty and ambiguity. Fuzzy system is a discipline that proved its capability to deal with vague problems. Converting traditional data to suit fuzzy system calculations is called fuzzification process which is a crucial step affecting the whole system accuracy? Generating appropriate membership function for fuzzy variables is one of the most challenging issues in fuzzy systems design. This paper proposes a solution to generate membership function automatically. An integrated hybrid model (PSO-TE) is introduced which benefit the Particle Swarm Optimization (PSO) and the information theory measures (entropy and mutual information) as the fitness function to adjust particles (membership function parameters). The proposed algorithm is tested using realistic planning information to show its effectiveness and efficiency. During the comparison between the PSO-TE and the fuzzy C-mean algorithm, the proposed model proved its ability to produce stable membership function for the telecommunication data barriers.

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“…In the prediction step, a linearized model of the system is used to infer the desired output and, later, in the update step, a correction is performed through the sensors. In addition, most EKF works are based on Euler angles to represent the orientation dynamics [14]. However, as it uses trigonometric relations, its computational cost becomes quite high.…”

Section: Introductionmentioning

confidence: 99%

An Embedded Quaternion-Based Extended Kalman Filter Pose Estimation for Six Degrees of Freedom Systems

Medeiros

Pimentel

Garibotti

2021

J Intell Robot Syst

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This paper proposes a formulation of quaternion-based Extended Kalman Filter pose estimation for six degrees of freedom systems embedded in an FPGA with commercial processors. Our approach uses the fusion of a camera and an inertial measurement unit to estimate simultaneously the position and the orientation of the system of interest. In addition, a Stewart platform is used to validate and evaluate the estimated pose. Although this work considers the use of common low-cost sensors and the use of markers with simple geometry, the results show excellent performance of the developed filter, being able to estimate the pose and orientation with an error below 8.14 mm and 0.63 o ¯, respectively. Furthermore, the effectiveness of the approach has also been evaluated, showing that the filter is able to converge quickly when the markers are retrieved after a loss of camera data for a short period of time.

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Particle filtering approach to membership function adjustment in fuzzy logic systems

Cited by 21 publications

References 35 publications

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Fuzzifying Telecommunication Network Features Basing PSO-TE Algorithm

An Embedded Quaternion-Based Extended Kalman Filter Pose Estimation for Six Degrees of Freedom Systems

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