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2021 ◽  
Vol 7 (167) ◽  
pp. 14-17
Author(s):  
V. Kostyuk

The article considers the method of modeling and factor analysis of the final production result of the enterprise – the volume of production. It is proposed to use deterministic models in the factor analysis of this indicator, which contain the set of factors that reflect the size and efficiency of use of the production areas of the enterprise. The article emphasizes that in the market economy, factor analysis is the important and effective basis for justifying management decisions. The main task of such analysis is the systematic and comprehensive study of the production activities of the enterprise in order to objectively assess the achieved results and establish real ways to further improve its efficiency. Taking this into account, the study of theoretical approaches to the performance of the factor analysis of the efficiency of using the production areas of the enterprise, that is, determining the quantitative influence of factors on the volume of production, is gaining relevance. To study the impact of these factors on the change of this indicator, it is proposed to conduct the step-by-step factor analysis, the essence of which is that the calculation of the impact on the change of the analyzed indicator is first two factors, then three and so on. This allows in each case to calculate the impact on the change of the studied indicator only the factors that are currently the most significant and relevant. The method of factor analysis of production volume given in the article gives the chance to define influence on its change of the most important factors reflecting the sizes and efficiency of use of the industrial areas occupied by the enterprise, to investigate patterns of such influence, to use this information at substantiation of administrative decisions.


2021 ◽  
Vol 7 (5) ◽  
pp. 2035-2044
Author(s):  
Man Lu

Objectives: How to construct an effective investment early warning model, scientifically and accurately predict China’s extreme financial risks, and thus formulate effective measures to deal with and prevent risks, has become an important issue urgently needed to be solved by financial risk management departments and investors. Based on multi-step factor analysis and artificial intelligence classification, two main intelligent investment models based on artificial intelligence are designed in this paper. Firstly, the principal component analysis method and multi-step factor extraction method are used to select the variables of 28 Financial Indicators of listed companies, and a multi-factor analysis investment model is constructed. Secondly, a smart single classifier based on factor analysis is designed. The experimental results show that combined with multi-step factor analysis has a better warning effect. The final research results show that the algorithm can guarantee the convergence performance and dispersion performance for different optimization problems, and has the advantages of stability and robustness, which embodies the application value of the algorithm.


Author(s):  
Cristina ȘERBĂNICĂ

"This study examines the territorial patterns of innovation in Romania, a country labelled as ‘modest innovator’. Our main assumption is that the large heterogeneity in the sub-national innovation patterns is not captured in the typologies developed for the NUTS2 regions. Consequently, the study proposes a categorization of the Romanian NUTS3 counties according to their innovation performance and structural characteristics, by means of a two-step factor analysis combined with hierarchical cluster analysis. The results point to the existence of five territorial groupings with similar characteristics: knowledge-intensive hubs, technology-intensive platforms, diversified agglomerations, industrial production zones and structurally challenged regions. Taken together, the results suggest the need to prioritize structural transformation and embrace the broad-based innovation concept."


Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Qibing Jin ◽  
Zhonghua Xu ◽  
Wu Cai

In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration. Firstly, different evolutionary strategies are integrated into different dimensions of the algorithm structure to improve the convergence accuracy and the randomization operation of the random Gaussian distribution is used to increase the diversity of the population. Secondly, special reinforcements are made to the process involving whales searching for prey to enhance their exclusive exploration or exploitation capabilities, and a new skip step factor is proposed to enhance the optimizer’s ability to escape the local optimum. Finally, an adaptive weight factor is added to improve the stability of the algorithm and maintain a balance between exploration and exploitation. The effectiveness and feasibility of the proposed REWOA are verified with the benchmark functions and different experiments related to the identification of the Hammerstein model.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Stéphanie M. E. van der Burgt ◽  
Rashmi A. Kusurkar ◽  
Janneke A. Wilschut ◽  
Sharon L. N. M. Tjin A Tsoi ◽  
Gerda Croiset ◽  
...  

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Stéphanie M. E. van der Burgt ◽  
Rashmi A. Kusurkar ◽  
Janneke A. Wilschut ◽  
Sharon L. N. M. Tjin A Tsoi ◽  
Gerda Croiset ◽  
...  

2019 ◽  
Vol 9 (16) ◽  
pp. 3308
Author(s):  
Zeng-You Sun ◽  
Yu-Jie Zhao

The Co-frequency Co-time Full Duplex (CCFD) is a key concept in 5G wireless communication networks. The biggest challenge for CCFD wireless communication is the strong self-interference (SI) from near-end transceivers. Aiming at cancelling the SI of near-end transceivers in CCFD systems in the radio frequency (RF) domain, a novel time-varying Least Mean Square (LMS) adaptive filtering algorithm which is based on step-size parameters gradually decrease with time varying called the DTV-LMS algorithm is proposed in this paper. The proposed DTV-LMS algorithm in this paper establishes the non-linear relationship between step factor and the evolved arct-angent function, and using the relationship between the time parameter and error signal correlation value to coordinately control the step factor to be updated. This algorithm maintains a low computational complexity. Simultaneously, the DTV-LMS algorithm can also attain the ideal characteristics, including the interference cancellation ratio (ICR), convergence speed, and channel tracking, so that the SI signal in the RF domain of a full duplex system can be effectively cancelled. The analysis and simulation results show that the ICR in the RF domain of the proposed algorithm is higher than that in the compared algorithms and have a faster convergence speed. At the same time, the channel tracking capability has also been significantly enhanced in CCFD systems.


Author(s):  
Jingfeng Zhang ◽  
Bo Han ◽  
Laura Wynter ◽  
Bryan Kian Hsiang Low ◽  
Mohan Kankanhalli

This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. Empirical evaluation on real-world datasets corroborates our analytical findings that a small h can indeed improve both its training and generalization robustness.


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