A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration

Zhao, Weian; Guo, S.L.; Zhou, Yun; Zhang, Jian

doi:10.1111/mice.12352

Cited by 27 publications

(9 citation statements)

References 23 publications

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“…In their book, Adeli and Hung () advanced adroit integration of ANN (Quintian & Corchado, ; Weissenberger, Meier, Lengler, Einarsson, & Steger, ) and GA (Li, Pu, Schonfeld, Yang, Zhang, Wang, and Xiong, ; Mencıa, Sierra, Mencıa, & Varela, ; Zhao, Guo, Zhou, & Zhang, ) with fuzzy logic (D'Urso, Masi, Zuccaro, & De Gregorio, ; Peng, Wang, Shi, Pérez‐Jiménez, & Riscos‐Núñez, ) for the solution of complex and intractable problems thus establishing the field of CI. Since then many neuro‐fuzzy algorithms have been developed and applied to different pattern recognition problems.…”

Section: Intelligent Controlmentioning

confidence: 99%

“…advanced adroit integration of ANN(Quintian & Corchado, 2017;Weissenberger, Meier, Lengler, Einarsson, & Steger, 2017) and GA(Li, Pu, Schonfeld, Yang, Zhang, Wang, and Xiong, 2017;Mencıa, Sierra, Mencıa, & Varela, 2016;Zhao, Guo, Zhou, & Zhang, 2018) with fuzzy logic(D'Urso, Masi, Zuccaro, & De Gregorio, 2018;Peng, Wang, Shi, Pérez-Jiménez, & Riscos-Núñez, 2017) …”

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confidence: 99%

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Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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Section: Intelligent Controlmentioning

confidence: 99%

mentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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“…Adaptive step size can overcome the shortcomings of low precision caused by fixed step size. Meanwhile, to avoid premature convergence, quantum computing (Zhao, Guo, Zhou, & Zhang, ) is introduced in this research, so that the diversity of the population is preserved, and the position of the individual bacterium is updated in the replication phase. Therefore, this paper proposes a novel smart bacteria‐foraging algorithm (SBFA) in combination with CKSVR.…”

Section: Related Workmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

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“…It can adjust the shape, size, and topology of structure to improve the structure performance (Kociecki & Adeli, , , ). In recent years, the development on genetic algorithms (Almeida & Pedrino, ; Kyriklidis & Dounias, ; Li et al., ; Padillo, Luna, Herrera, & Ventura, ; Zhao, Guo, Zhou, & Zhang, ) provides more alternatives for form generation. Some researchers have used structural optimization technology to realize the form generation methods for RGS.…”

Section: Introductionmentioning

confidence: 99%

Self‐adaptive form generation method for reciprocal grid structures

Qiao³

et al. 2018

Computer aided Civil Eng

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Reciprocal grid structure is a structural system consisting of mutually supporting bars.Its geometry should satisfy not only the demands of architectural intents but also the requirements of the lapping relationship of the bars. Thus, form generation is the key problem in its design. Inspired by the physical building process of a reciprocal grid structure, self-adaptive form generation method is proposed. In this method, an initial plane grid with the topology of required structural configuration is created first. Next, elements are iteratively lapped to realize a self-adaptive generation of structural form. Two new parameters, bar lapping factor and rotation angle, are proposed to describe reciprocal grid structures quantitatively and make the form generation process easy. The efficiency and accuracy of the method are illustrated by comparisons with other form generation methods. Numerical examples demonstrate the applicability of the proposed method in the complex and realistic reciprocal grid structures.

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A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration

Cited by 27 publications

References 23 publications

Control methodologies for vibration control of smart civil and mechanical structures

Control methodologies for vibration control of smart civil and mechanical structures

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

Self‐adaptive form generation method for reciprocal grid structures

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