The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on t...
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The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs.
The large-scale connection of photovoltaic (PV) power generation to the power grid introduces considerable challenges to the grid's stable operation. To ensure grid stability, the key is to improve the accuracy of...
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The large-scale connection of photovoltaic (PV) power generation to the power grid introduces considerable challenges to the grid's stable operation. To ensure grid stability, the key is to improve the accuracy of PV power prediction. Currently, PV power prediction is primarily dependent on single-model construction, which ignores the role of data processing technologies, thus resulting in inadequate prediction performance. Accordingly, in this study, a combined prediction system is developed by integrating advanced deep learning algorithms and data processing techniques to improve the accuracy of PV power prediction. In addition, a nonlinear weighting strategy based on an improved multi-objective dragonfly optimizationalgorithm (IMODA) is proposed to determine the final prediction result. In the IMODA, cloud model generator, tent mapping, and Pareto solution selection strategy based on knee points are introduced to resolve the defects of the original algorithm. The performance of the proposed combined system was scientifically evaluated and analysed by considering the PV power datasets of four seasons in Belgium as an example. The mean absolute percentage errors of the proposed system on the four datasets are 4.0198, 4.7943, 4.3587, and 5.9286. These values indicate an improvement range of 5%-30% compared with the errors of other benchmark models.
multi-blade centrifugal fans are the main workhorse of automotive air conditioners, and the performance of these fans affects riding comfort. This article proposes a prediction model and a multi-objectiveoptimization...
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multi-blade centrifugal fans are the main workhorse of automotive air conditioners, and the performance of these fans affects riding comfort. This article proposes a prediction model and a multi-objective optimization algorithm and applies them to the optimization design of a multi-blade centrifugal fan. A prediction model between the design variables and optimizationobjectives, named wPSO-BP, is proposed, and the model is more effective than the BP prediction model in predicting fan performance. A multi-objective optimization algorithm, named NSGA-III-LBWO, is proposed and applied to the optimization design of the fan along with the wPSO-BP prediction model. The results indicate that the aerodynamics and noise performance of the optimized fan were improved, which provides a reference for the optimized design of these types of fans.
Electricity load prediction is of great significance to the development of the power market and stable operation of power systems. In recent years, scholars in this field have only considered point forecasting, which ...
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Electricity load prediction is of great significance to the development of the power market and stable operation of power systems. In recent years, scholars in this field have only considered point forecasting, which ignores the inevitable prediction bias and uncertain information. To fill this gap, this study proposes an interval prediction system consisting of an advanced data reconstruction strategy, a multi-objective optimization algorithm based on the theory of non-negative constraints, and an outstanding interval forecasting model fitted by the predicted fluctuation characteristics. Moreover, this study theoretically proves that the weight assigned by the optimizationalgorithm is the Pareto optimal solution. Empirical data with 30 min intervals from Queensland, Australia are selected as samples for research. The results not only demonstrate the superiority of the proposed model but also provide effective technical support for power grid operation and dispatch by quantifying changes in the prediction results caused by uncertainties.
作者:
Zhanhong WuCuili YangBeijing University of Technology
Faculty of Information TechnologyBeijing Key Laboratory of Computational Intelligence and Intelligent SystemEngineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education
In this paper,a new approach for optimizing the structure and prediction error of echo state network(ESN) is *** is a kind of recurrent neural network with simple training and strong generalization *** is an importa...
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In this paper,a new approach for optimizing the structure and prediction error of echo state network(ESN) is *** is a kind of recurrent neural network with simple training and strong generalization *** is an important structure of ESN,which determine network ***,multi-objective optimization algorithm is used to optimize network structure and training error ***,a local search algorithm based on l regularization is used to accelerate *** experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numerical simulations. Displacement back-analysis is the most widely used method; however, the reliability of the current approa...
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Accurate determination of rock mass parameters is essential for ensuring the accuracy of numerical simulations. Displacement back-analysis is the most widely used method; however, the reliability of the current approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameter back-analysis method, that considers the construction process and displacement losses is proposed and implemented through the coupling of numerical simulation, auto-machine learning (AutoML), and multi-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanized twin tunnels is developed, generating a dataset through extensive numerical simulations. Next, the AutoML method is utilized to establish a surrogate model linking rock parameters and displacements. The tunnel construction process is divided into multiple stages, transforming the rock mass parameter back-analysis into a multi-objectiveoptimization problem, for which multi-objective optimization algorithms are introduced to obtain the rock mass parameters. The newly proposed rock mass parameter back-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectiveness are demonstrated. Compared with traditional single-stage back-analysis methods, the proposed model decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving the accuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increases with the number of construction stages considered, the back analysis time is acceptable. This study provides a new method for displacement back analysis that is efficient and accurate, thereby paving the way for precise parameter determination in numerical simulations.
Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting...
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Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise-a multi-objective grasshopper optimizationalgorithm based on a no-negative constraint theory-and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models.
The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, gr...
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The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, greenhouse gas emissions and energy efficiency levels of lighting and air conditioning system, according to the mandatory technical regulation in public buildings in Brazil. Presenting a solution to assist in the decision making of engineers, architects or building managers for the optimal arrangements' choice for lighting and air conditioning equipment, considering each built environment and project profile. For the validation process, a basic building was created with 15 rooms spread over three floors, according to the most common construction parameters in the North of Brazil. First, different combinations of objective-function candidates were investigated to compose the multi-objectivealgorithm fitness function, analyzing its performance in two central scenarios: (1) adding some "baits" in air conditioning equipment files, and (2) without this inclusion. Thus, it was found that considering only three objective functions-incremental cost, energy consumption and the air conditioning energy efficiency coefficient-it is possible to get optimal non-dominated solutions in both scenarios, thus highlighting the robustness of the proposed methodology.
In view of the improved algorithm MOEA/D-AU based on the framework of the decomposition based multiobjectiveoptimizationalgorithm framework (MOEA/D), an adaptive dynamic selection angle adjustment strategy is intro...
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ISBN:
(纸本)9781728100159
In view of the improved algorithm MOEA/D-AU based on the framework of the decomposition based multiobjectiveoptimizationalgorithm framework (MOEA/D), an adaptive dynamic selection angle adjustment strategy is introduced to balance between convergence and diversity. This paper proposed an adaptive angle selection multi-objective optimization algorithm, MOEA/D-AAU. The algorithm adaptively adjusts the angle range selection coefficient G in the MOEA/D-AU algorithm by using the appropriate dynamic adjustment strategy, which makes the algorithm focus on the convergent back propagation dispersion in the convergence process. Finally, the performance of proposed algorithm is compared with four the state of the art algorithms on DTLZ and WFG benchmark function. Experiments result demonstrated that MOEA/D-AAU algorithm can achieve better Pareto-optimal solutions and obtain a good convergence and diversity in solution space.
In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand....
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In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimizationalgorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.
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