Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization

Wang, Jiasen; Wang, Jun; Che, Hangjun

doi:10.1109/tnnls.2019.2918984

Cited by 36 publications

(5 citation statements)

References 38 publications

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“…In [16], the LSTM deep neural network model was built to predict stock price volatility, and financial time series analysis was introduced on top of it to build a hybrid model to predict the closing price; the prediction results showed that the hybrid model had significantly improved prediction performance compared with traditional time series analysis and neural network models. e authors in [17] used LSTM deep neural networks for forecasting short-term trends in the stock market.…”

Section: Deep Learning In the Stock Marketmentioning

confidence: 99%

A Study of Stock Market Predictability Based on Financial Time Series Models

2022

Mobile Information Systems

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In today’s era of economic globalization and financial integration, the stock market is constantly complex, showing many deviations that cannot be explained by classical financial analysis, but at the same time, some classic financial statistical features have striking similarities. This suggests that although the stock market is intricate, there are universal laws that can be found through data mining to find its underlying operating rules. In this paper, we construct financial time series models such as ARIMA, ARCH, and GARCH to predict the stock market price fluctuations and trends. The ARIMA model is used to fit the linear financial time series, and the GARCH model is used to fit the nonlinear time series residuals. The results show that the integrated tree model based on the idea of weight voting has high accuracy in predicting stock market bulls and bears, with XGBoost prediction accuracy up to 96%, and the neural network model is also very effective, with an accuracy rate of over 90%.

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Section: Deep Learning In the Stock Marketmentioning

confidence: 99%

A Study of Stock Market Predictability Based on Financial Time Series Models

2022

Mobile Information Systems

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“…Reference [17] proposed a context loss to achieve semantic style transformation with free segmentation. Reference [18] speeded up the original style migration method by optimizing the feature space instead of the pixel space. Reference [19] smooths the boundary between the target object and the background after local migration by adding a Markov random field-based loss.…”

Section: Related Workmentioning

confidence: 99%

Animation Costume Style Migration Based on CycleGAN

Zhang

Lee

2022

Wireless Communications and Mobile Computing

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The traditional style migration of animation costumes is mainly performed between two paired animation costumes. However, the generalization ability is weak, and the migration effect is not good when the gap between the training and testing costumes is large. To address the above problems, this paper proposes a style migration method for animated costumes combining full convolutional network (FCN) and CycleGAN, which enables the instance style migration between animated costumes with specific targets. It is also verified that the training dataset is not the factor that causes the poor style migration of CycleGAN. The experiments demonstrate that the animation costume style migration method combining full convolutional network and CycleGAN increases the recognition ability and can achieve the local style migration of the animation costume while maintaining the integrity of the rest of the elements, and compared with CycleGAN, the method can effectively suppress the style migration in areas outside the target.

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“…Not only internal quality system construction is reflected in the policy and curriculum system of arts vocational education but also teachers for leading education and students as the subject of education also play an important role in education, strengthening the teacher talent training model and thus promoting the development of arts vocational schools. Voice teaching exploration and growth become an important guarantee of internal system construction in the new era of arts vocational institutions [17]. To this end, we need to use digital technology to identify, access, and respond to common and unexpected problems in the internal quality management of teaching in a timely and accurate manner; to react, reflect, and continue in-depth research in the first instance; and to promote the establishment of a highly qualified, innovative, and professional teaching staff.…”

Section: The Integration Of Digital Technology Andmentioning

confidence: 99%

Analysis and Optimization of the Online Vocal Teaching System Based on Intelligent Computing

Jiang

2022

Mathematical Problems in Engineering

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With the continuous development of science and technology, network technology has become more and more advanced and network applications are widely used in all walks of life, and vocal music teaching is no exception. The Intelligent Agent plays a unique role in networked teaching by making up for the lack of intelligence, adaptiveness, autonomy, and interoperability and interactivity in all aspects of traditional teaching. This paper introduces the knowledge of Intelligent Agent and then discusses its application in the networked teaching environment. Compared with the traditional teaching environment, the intelligent network teaching environment is more conducive to the initiative and innovation of students, reflecting the characteristics of student-centred learning and achieving a variety of functions such as teaching resource sharing, information interaction, online communication, and distance learning with the support of computer network technology. It is a new field to be explored, especially opening up a new chapter of online vocal music teaching.

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Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization

Cited by 36 publications

References 38 publications

A Study of Stock Market Predictability Based on Financial Time Series Models

A Study of Stock Market Predictability Based on Financial Time Series Models

Animation Costume Style Migration Based on CycleGAN

Analysis and Optimization of the Online Vocal Teaching System Based on Intelligent Computing

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