In recent years, IT managers of large enterprises and stakeholders have turned to cloud computing due to the benefits of reduced maintenance costs and security concerns, as well as access to high-performance hardware ...
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In recent years, IT managers of large enterprises and stakeholders have turned to cloud computing due to the benefits of reduced maintenance costs and security concerns, as well as access to high-performance hardware and software resources. The two main challenges that need to be considered in terms of importance are ensuring that everyone has access to services and finding efficient allocation options. First, especially with software services, it is very difficult to predict every service that may be needed. The second challenge is to select the best independent service among different providers with features related to application reliability. This paper presents a framework that uses the particleswarmoptimization technique to optimize reliability parameters in distributed systems applications. The proposed strategy seeks a program with the best service and a high degree of competence. Although this method does not provide an exact solution, the particleswarmoptimizationalgorithm reaches a result close to the best solution and reduces the time required to adjust the parameters of distributed systems applications. The results of the work have been compared with the genetic algorithm and it has been shown that the PSO algorithm has a shorter response time than both the genetic algorithm and the PSO. Also, the PSO algorithm shows strong stability and ensures that the solution obtained from the proposed approach will be close to the optimal solution.
Abstract. With the rapid development of information management means in domestic large and medium-sized enterprises, information technology has become an important support and means to improve the level of enterprise ...
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ISBN:
(纸本)9781450399548
Abstract. With the rapid development of information management means in domestic large and medium-sized enterprises, information technology has become an important support and means to improve the level of enterprise management. At the same time, standardizing the management of information infrastructure has become a more and more prominent problem. This paper summarizes the chaotic particle swarm optimization algorithm and financial sharing services, discusses the construction of particleswarmoptimizationalgorithm and the development of intelligent finance, analyzes the implementation of the blueprint of accounting information decision-making system, and studies the regional fiscal expenditure of China, the United States and the United States, Japan and Russia. The results show that China's financial expenditure ranks first in the whole company.
The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has incr...
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The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world's financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company's stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public's awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaoticparticleswarmalgorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaoticparticleswarmoptimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaoticparticleswarmoptimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.
Study the basic theory and process of chaos particleswarmoptimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical mod...
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ISBN:
(纸本)9783037858417
Study the basic theory and process of chaos particleswarmoptimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particleswarmoptimizationalgorithm is effective and feasible to solve the logistics scheduling model problem.
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall i...
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ISBN:
(纸本)9781450354141
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particleswarmoptimizationalgorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particleswarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.
For pursuing economic and environmental-friendly goals, this paper constructs a regional integrated micro energy system where electricity, heat, cooling, and gas energies are interacted and integrated among buildings....
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For pursuing economic and environmental-friendly goals, this paper constructs a regional integrated micro energy system where electricity, heat, cooling, and gas energies are interacted and integrated among buildings. First, independent and collaborative operations of micro energy systems are proposed. Second, seasonal and daily power, heat, and cooling loads of a residential building, a business building, and a mall are depicted. Third, in order to minimize the operation cost, energy consumption and CO2 emission, a multi-energy coordinated flexible operation optimization model of integrated micro energy system is established, and the chaotic particle swarm optimization algorithm is applied to solve the optimization model. A benefit evaluation model is built based on perspectives of energy, economy, and environment to evaluate the optimization results. Then, Shapley method is improved by integrating it with cloud focus theory to more fairly allocate optimized benefits, and influencing indexes for adjusting the weights of participants are proposed. Finally, a case study is conducted. The results indicate that: (1) the integrated micro energy system enabled surplus energy inter-supply among the subsystems, thus realizing interconnection and energy complementarity between micro energy systems;(2) seasonal load characteristics showed the differences among the building subsystems, where the winter load showed more flexibility in energy conversion and inter-supply among the subsystems than the summer load;(3) the improved Shapley method are more effective and fairer for benefit allocation, based on different level of importance of participants;(4) chaotic particle swarm optimization algorithm are more superior in terms of calculation efficiency and accuracy for optimization solutions. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Aiming at the problems of complex topology and less fault samples in digital power grid, a fault diagnosis method based on improved radial basis function and back propagation (RBF-BP) neural network is proposed. First...
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ISBN:
(纸本)9781665490542
Aiming at the problems of complex topology and less fault samples in digital power grid, a fault diagnosis method based on improved radial basis function and back propagation (RBF-BP) neural network is proposed. Firstly, build a digital power grid architecture, and realize the functions of each application of the system through the collection and analysis of the data of the physical equipment of the power grid. Then, the RBF-BP neural network is improved by using the chaotic particle swarm optimization algorithm, and the grid data is input into the optimal network model to complete the accurate diagnosis of fault types. Finally, based on the small current grounding fault simulation platform, the proposed method is demonstrated by experiments. The results show that the diagnostic accuracy and diagnostic time are 95.69% and 197 ms respectively, which can efficiently complete the fault diagnosis of digital power grid.
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall i...
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Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particleswarmoptimizationalgorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particleswarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.
Data-based variable universe adaptive fuzzy controllers (VUAFCs) with self-tuning parameters are developed for complex systems with unknown universes in this paper. The main feature of the proposed VUAFC is the new de...
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Data-based variable universe adaptive fuzzy controllers (VUAFCs) with self-tuning parameters are developed for complex systems with unknown universes in this paper. The main feature of the proposed VUAFC is the new defined contraction-expansion (C-E) factor on an infinite universe, which is more flexible and practical in real applications. Moreover, the data-based methods to tune the parameters including the peak points of output fuzzy subsets and the C-E factor parameters are proposed. The peak points of the output fuzzy subsets are mined by an improved Wang-Mendel method based on conflicting rules. The parameters of the VUAFC are optimized by solving an offline optimization problem using the chaoticparticleswarmoptimization (CPSO) algorithm. The simulation results on the strip temperature control of the radiant-tube indirect-fired furnace of annealing furnace show that our proposed method has strong practicability and good control performance. (C) 2022 Elsevier B.V. All rights reserved.
Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Co...
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Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaoticparticleswarmoptimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting. (C) 2010 Elsevier Ltd. All rights reserved.
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