Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

Zeroing neural dynamics (ZND), a special class of neural dynamics, is a powerful methodology for time-varying problems solving. On the basis of this methodology, different continuous-time ZND models are obtained for various time-varying problems solving. Continuous-time ZND models are supposed to be discretized for the sake of prevalent digital-equipment applications, and a discretization formula is needed to transform a continuous-time ZND model into a discrete-time ZND model. In this article, continuous-time ZND models, new discretization formulas and various discrete-time ZND models are presented. The time-varying minimization problem, which is a representative time-varying issue, is also discussed as an example throughout this article. The relationship between ZND and Zhao-Lu-Swamy-Feng (ZLSF) models is identified; i.e., the ZLSF models are minimization-type and Euler-type special cases of ZND models. In addition, ZND models are compared with other models to demonstrate their differences. The article aims to introduce the ZND methodology and illustrate the manner by which it is used, provide readers with new discretization formulas and various continuous-time and discrete-time ZND models for time-varying problems solving, discuss the factors affecting the performance of the aforementioned models, exemplify the differences between ZND models and other models, and point out future research directions.

Time-variant problems, which can be classified into future problems and non-future problems, are often encountered in academia and industry. In a future problem, we only know the information of current and past time instants, and we have to acquire the next-time-instant solution before the next time instant arrives. Zeroing neural dynamics (ZND) and Zhang et al. discretization (ZeaD) formula group are two essential tools to build discrete-time ZND (DT-ZND) models for future problems solving. The former uses a systematical design formula to build a continuous-time ZND (CT-ZND) model, and the latter is used to transform the CT-ZND model into discrete-time forms. Many DT-ZND models have been developed to solve various time-variant problems. In light of DT-ZND models and correction strategy, in this paper, we mainly focus on designing and building improved models for future problems solving. Based on ZND method, extrapolation formulas and correction steps, new models and the corresponding computational algorithms are proposed to solve future optimization and future matrix inversion problems. Numerical experiments are also carried out to demonstrate the superiority of the proposed algorithms.