Towards adaptive multi-robot systems: self-organization and self-adaptation

Hrabia, Christopher-Eyk; Lützenberger, Marco; Albayrak, Şahin

doi:10.1017/s0269888918000176

Cited by 10 publications

(9 citation statements)

References 85 publications

(151 reference statements)

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“…In the field of image and natural language processing, DL (deep learning) [6] has made a lot of achievements, and many model structures, such as the convolution BPNN structure in the image field and the cyclic BPNN structure of speech, have become the best or general solutions in this field [7]. BPNN (BP neural network), as an artificial intelligence algorithm, has strong nonlinear mapping ability, self-organization, and self-adaptation ability [8]. e prediction model combined with BPNN and gray prediction has good pertinence to the prediction fields with nonlinearity, uncertainty, and less historical data [9,10].…”

Section: Introductionmentioning

confidence: 99%

Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning

Wang¹

2022

Wireless Communications and Mobile Computing

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With the development of higher education in full swing, the number of college students in China is increasing, the employment pressure of college students is increasing, and the employment situation in universities is not optimistic. Subjective career obstacles are obstacles that individuals may encounter when they perceive themselves according to their own conditions and surrounding environmental factors based on their future career pursuit and goals. In this paper, it is of practical significance to use the employment confidence index of college students to analyze and predict their employment confidence. Based on DL (deep learning), a model GM-BPNN (Gray model-BP neural network) of subjective employment obstacles of college students and its predictive factors is proposed. Initially, the employment data of a university are collected and normalized. Then, GM and BPNN are used to model and predict the number of college students’ employment from different angles. Finally, the weights of the prediction results of GM and BPNN are determined, and the final prediction results of the number of college students’ employment are obtained by weighting. The results show that the relative error of the combined model is smaller and the accuracy is higher.

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Section: Introductionmentioning

confidence: 99%

Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning

Wang¹

2022

Wireless Communications and Mobile Computing

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“…Research into self-organization is plentiful, especially in MASs [223,224]. In complex networks, self-organization is often regarded as a modeling problem [225][226][227][228].…”

Section: Emergence and Consensus Through Decentralized Control In Com...mentioning

confidence: 99%

Engineering Emergence: A Survey on Control in the World of Complex Networks

Berceanu

Pătraşcu

2022

Automation

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Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.

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“…Thus, it is reasonable to expect that our approach could be applied to them as well. Simulation is still widely used in the context of multi-agent systems [31,56,62,69,82,91] for bug-finding and quick feedback [51], but due the presence of concurrency and asynchrony it may only be marginally effective in many cases [104].…”

Section: Related Workmentioning

confidence: 99%

Verification of Distributed Systems via Sequential Emulation

Stefano

Nicola

Inverso

2022

ACM Trans. Softw. Eng. Methodol.

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Sequential emulation is a semantics-based technique to automatically reduce property checking of distributed systems to the analysis of sequential programs. An automated procedure takes as input a formal specification of a distributed system, a property of interest, and the structural operational semantics of the specification language and generates a sequential program whose execution traces emulate the possible evolutions of the considered system. The problem as to whether the property of interest holds for the system can then be expressed either as a reachability or as a termination query on the program. This allows to immediately adapt mature verification techniques developed for general-purpose languages to domain-specific languages, and to effortlessly integrate new techniques as soon as they become available. We test our approach on a selection of concurrent systems originated from different contexts from population protocols to models of flocking behaviour. By combining a comprehensive range of program verification techniques, from traditional symbolic execution to modern inductive-based methods such as property-directed reachability, we are able to draw consistent and correct verification verdicts for the considered systems.

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Towards adaptive multi-robot systems: self-organization and self-adaptation

Cited by 10 publications

References 85 publications

Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning

Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning

Engineering Emergence: A Survey on Control in the World of Complex Networks

Verification of Distributed Systems via Sequential Emulation

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