Optimum Design of a Composite Optical Receiver by Taguchi and Fuzzy Logic Methods

Wang, Ning; Peng, Xing

doi:10.3390/mi12121434

Cited by 5 publications

(7 citation statements)

References 37 publications

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“…In the FL system, fuzzy rules are used to create a fuzzy rule base that defines the correlation between the input and output data of a system (Kuo & Lin, 2019). The Mamdani, a linguistic fuzzy rule, is used to make inferences (Wang et al, 2021). In the fuzzy inference process, a variety of different membership functions, including triangle, trapezoid, and Gaussian can be applied to input and output variables.…”

Section: Methodsmentioning

confidence: 99%

“…In this article, a total of nine rules which were sufficient to get optimum values were employed (Table 3). The “if‐then” rules of fuzzy inference can be expressed as follows:

\{\begin{matrix} Rule 1 : if a_{1} {is}^{“} {X_{1}}^{”} and a_{2} {is}^{“} {Y_{1}}^{”} then b is Z_{1}; \\ Rule 2 : if a_{1} {is}^{“} {X_{2}}^{”} and a_{2} {is}^{“} {Y_{2}}^{”} then b is Z_{2}; \\ \dots \\ Rule 9 : if a_{1} {is}^{“} {X_{9}}^{”} and a_{2} {is}^{“} {Y_{9}}^{”} then b is Z_{9}; \end{matrix}\}

where a 1 and a 2 are FC and FS, respectively; b is MPCI; and membership functions of X i , Y i , and Z i are μ Xi (S, M, and L), μ Yi (S, M, and L), and μ Zi (VL, L, M, H, and VH), respectively (Wang et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Fuzzy rules were evaluated with the Mamdani implication method and then converted into a fuzzy reasoning output (Figure 2). The membership function of the fuzzy output can be represented as follows (Equation 5):

μ_{c_{0} (b)} = (μ_{X_{1}} (a_{1}) \land μ_{Y_{1}} (a_{2}) \lor \dots (μ_{X_{9}} (a_{1}) \land μ_{Y_{9}} (a_{2})))

where

⋀

and

⋁

are the min and max operations, respectively (Wang et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…It generates a specific value that represents the central location of the possible range of values in the output of the inference process (Raja et al, 2019). Thus, the centroid defuzzification method was applied to convert the fuzzy inference output [ μ c0 ( b i )] into a nonfuzzy value (MPCI), and expressed as Equation (6) (Wang et al, 2021).

MPCI = \frac{\sum_{i = 1}^{k} b_{i} μ_{c 0} (b_{i})}{\sum_{i = 1}^{k} μ_{c 0} (b_{i})}

…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing foam quality characteristics of model food using Taguchi‐based fuzzy logic method

Güldane

2023

J Food Process Engineering

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In this study, the effects of soy protein isolate (SPI) concentration (0%, 50%, and 100%), sonication time (10, 20, and 30 min), and whipping time (5, 10, and 15 min) on the quality characteristics of the model food foam were investigated and optimized. In order to maximize both foaming capacity (FC) and foam stability (FS), it was aimed to select the optimum process parameters using a Taguchi‐based fuzzy logic method (TBFLM). The experiments were performed according to Taguchi L9 (33) orthogonal design, and the effects of process parameters on foam quality characteristics were investigated by analysis of variance. Optimization results revealed that optimum FC and FS values were obtained with 50% SPI, 30 min sonication, and 10 min whipping. SPI concentration was the most effective factor for the responses in Taguchi single‐response optimization. However, the whipping time has the greatest impact on the multiperformance characteristics index, accounting for 78.93% of the contribution to fuzzy logic multiresponse optimization. The verification experiments revealed that the TBFLM is a useful method for improving foam quality characteristics. Practical applications FC and FS are the most important properties, which represent the quality characteristics of model food foams. The Taguchi method failed in multiresponse optimization in the model food application. A Taguchi‐based Fuzzy logic optimization method was successfully applied to optimize the quality characteristics of model food foam, and the optimal conditions were determined as soy protein isolate of 50%, sonication time of 30 min, and whipping time of 10 min. The contribution of whipping time to the multiperformance characteristics index was found to be the highest.

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Section: Methodsmentioning

confidence: 99%

“…In this article, a total of nine rules which were sufficient to get optimum values were employed (Table 3). The “if‐then” rules of fuzzy inference can be expressed as follows:

\{\begin{matrix} Rule 1 : if a_{1} {is}^{“} {X_{1}}^{”} and a_{2} {is}^{“} {Y_{1}}^{”} then b is Z_{1}; \\ Rule 2 : if a_{1} {is}^{“} {X_{2}}^{”} and a_{2} {is}^{“} {Y_{2}}^{”} then b is Z_{2}; \\ \dots \\ Rule 9 : if a_{1} {is}^{“} {X_{9}}^{”} and a_{2} {is}^{“} {Y_{9}}^{”} then b is Z_{9}; \end{matrix}\}

Section: Methodsmentioning

confidence: 99%

μ_{c_{0} (b)} = (μ_{X_{1}} (a_{1}) \land μ_{Y_{1}} (a_{2}) \lor \dots (μ_{X_{9}} (a_{1}) \land μ_{Y_{9}} (a_{2})))

where

⋀

and

⋁

are the min and max operations, respectively (Wang et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

MPCI = \frac{\sum_{i = 1}^{k} b_{i} μ_{c 0} (b_{i})}{\sum_{i = 1}^{k} μ_{c 0} (b_{i})}

…”

Section: Methodsmentioning

confidence: 99%

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Optimizing foam quality characteristics of model food using Taguchi‐based fuzzy logic method

Güldane

2023

J Food Process Engineering

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“…The optimization of HSMs is very difficult because of the complexity of the motor structure, the small air gap of the motor, and the presence of both axial and radial magnetic fields [ 3 , 4 , 5 , 6 ]. The three-dimensional finite element method (FE method, FEM) is an effective tool for the analysis of such motors [ 4 ], but they are extremely computationally intensive and require very long computation times, making them ineffective for the optimization of HSMs.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Three-Phase Hybrid Stepper Motors for Noise Reduction

Peng

Fang

et al. 2022

Sensors

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For the optimization of three-phase hybrid stepper motors with complex electromagnetic structures, an optimization method is presented in this paper. The method is a combination of 3D-FEM and the Taguchi optimization method intended to reduce the dependence on FEM results during the optimization calculation. In this paper, the optimization method is used in the optimization of the tooth shape of the three-phase hybrid stepper motor, and the objective is to reduce the noise caused by harmonics in the “torque-angle characteristic” of the motor. It is clear that traditional optimization methods make it very difficult to carry out such an optimization calculation as a large number of finite element calculations have to be used in the optimization process, and the required computation time is extremely long. Using the optimization method presented in the paper, the optimization becomes feasible because the number of finite element calculations is greatly reduced and the computation time is thus greatly reduced. In order to check the effectiveness of the optimization, the waterfall diagram for noise analysis and its application to check torque ripple are also presented in the paper. Both simulation and test results show that the optimized structure can significantly reduce the motor noise caused by torque ripple. Therefore, the optimization method proposed in this paper can be an effective tool for the optimal design of high-performance motors, including stepper motors.

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Cross-linking of polyvinyl alcohol-g-methionine with bisacrylamide and optimization of its swelling response using taguchi method

Sabirneeza

Gowsalya

Harinivashini

2023

Materials Today: Proceedings

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Optimum Design of a Composite Optical Receiver by Taguchi and Fuzzy Logic Methods

Cited by 5 publications

References 37 publications

Optimizing foam quality characteristics of model food using Taguchi‐based fuzzy logic method

Optimizing foam quality characteristics of model food using Taguchi‐based fuzzy logic method

Optimization of Three-Phase Hybrid Stepper Motors for Noise Reduction

Cross-linking of polyvinyl alcohol-g-methionine with bisacrylamide and optimization of its swelling response using taguchi method

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