Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated t...Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.展开更多
The aluminum alloy structure impact localization system by using fiber Bragg grating （FBG） sensors and impact localization algorithm was investigated. A four-FBG sensing network was established. And the power intens...The aluminum alloy structure impact localization system by using fiber Bragg grating （FBG） sensors and impact localization algorithm was investigated. A four-FBG sensing network was established. And the power intensity demodulation method was initialized employing the narrow-band tunable laser. The wavelet transform was used to weaken the impact signal noise. And the impact signal time difference was extracted to build the time difference localization algorithm. At last, a fiber Bragg grating impact localization system was established and experimentally verified. The experimental results showed that in the aluminum alloy plate with the 500mm＊500mm＊2mm test area, the maximum and average impact abscissa localization errors were 11 mm and 6.25mm, and the maximum and average impact ordinate localization errors were 9 mm and 4.25 mm, respectively. The fiber Bragg grating sensors and demodulation system are feasible to realize the aviation aluminum alloy material structure impact localization. The research results provide a reliable method for the aluminum alloy material structure impact localization.展开更多
基金supported by the National Natural Sci-ence Foundation of China(62006184,62076189,61873277).
文摘Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.
文摘The aluminum alloy structure impact localization system by using fiber Bragg grating （FBG） sensors and impact localization algorithm was investigated. A four-FBG sensing network was established. And the power intensity demodulation method was initialized employing the narrow-band tunable laser. The wavelet transform was used to weaken the impact signal noise. And the impact signal time difference was extracted to build the time difference localization algorithm. At last, a fiber Bragg grating impact localization system was established and experimentally verified. The experimental results showed that in the aluminum alloy plate with the 500mm＊500mm＊2mm test area, the maximum and average impact abscissa localization errors were 11 mm and 6.25mm, and the maximum and average impact ordinate localization errors were 9 mm and 4.25 mm, respectively. The fiber Bragg grating sensors and demodulation system are feasible to realize the aviation aluminum alloy material structure impact localization. The research results provide a reliable method for the aluminum alloy material structure impact localization.
