scholarly journals Neural network approximation in forecasting economic risks

2021 ◽  
Vol 5 (S4) ◽  
Author(s):  
Kateryna I. Kotsiubivska ◽  
Olena V. Tymoshenko ◽  
Olena A. Chaikovska ◽  
Maryna S. Tolmach ◽  
Svitlana S. Khrushch
Keyword(s):  
Neural Network ◽  
Economic Development ◽  
Forecast Model ◽  
Economic Systems ◽  
Approximation Model ◽  
The Neural Network ◽  
Risk Forecasting ◽  
Methodological Approaches ◽  
The Impact ◽  
Macroeconomic Environment

The article considers methodological approaches to assessing the level of development of economic systems in the context of increasing the accuracy of forecasts in unpredictable socio-economic conditions in particular taking into account the impact of unforeseen environmental risks and disasters. The authors used methods to approximate economic criteria with the help of neural networks. Analyzing the criteria of economic development of different countries, as well as taking into account the factors of the macroeconomic environment, a neural network approximation model of risk forecasting in the economic development of the country has been developed. To date, a large number of mathematical forecasting methods are known, and experts in the world economy use appropriate risk assessment criteria, but the neural network is used when the exact type of connections between inputs and outputs is unknown, which allows us to create a more accurate and flexible forecast model. The modeling takes into account the main weights that determine the degree and the priority of the impact on each component of the economic system and characterizes the complex macroeconomic relationships to determine the aggregate indices.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

2020 ◽  
Author(s):  
James Dallas ◽  
Yifan Weng ◽  
Tulga Ersal
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Soft Soil ◽  
Computation Time ◽  
Operating Conditions ◽  
The Neural Network ◽  
Vehicle Response ◽  
The Impact

Abstract In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

Generalized Regression Neural Network Cargo Flow Forecast Model in Logistics Park Based on Radial Basis Function

2011 ◽  
Vol 287-290 ◽  
pp. 622-625
Author(s):  
Qin Zhang ◽  
Yin Kui Liang ◽  
Wen Xia Yu ◽  
Qiang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Forecast Model ◽  
Scientific Basis ◽  
Generalized Regression Neural Network ◽  
Chain Management ◽  
Logistics Park ◽  
Generalized Regression ◽  
The Impact ◽  
Flow Forecast

According to cargo flow, the strength of the logistics supply need could be predicted. Improving predicting accuracy can provide a scientific basis for the construction and operation on the logistics park. Generalized regression neural network model of logistics park is introduced under the impact of supply chain management, and designing steps about the prediction model is given. And the prediction model predicts Jinan Gaijiagou Logistics Park well.

scholarly journals Globalized Inclination to Acquire Knowledge and Skills Toward Economic Development

2021 ◽  
Vol 18 ◽  
pp. 1349-1369
Author(s):  
Grigorios L. Kyriakopoulos
Keyword(s):  
Economic Development ◽  
Economic Recession ◽  
Point Of View ◽  
Economic Systems ◽  
Skills Acquisition ◽  
Highly Qualified ◽  
Cultural Characteristics ◽  
Knowledge And Skills ◽  
E Learning ◽  
The Impact

In an era of economic recession and the divulged threats of COVID-19 pandemic in the world a highly impacting socio-economic activity is the education sector. The operational difficulties of companies and organizations, as well as the closure of universities, schools, training courses, are all affecting the entrepreneurial and the learning progress on using workers time and learners facilities to develop their knowledge and to build up their skills. In particular, free time of institutions’ closure can become a golden opportunity for learning and progresses in virtual education while adopting e-learning modes of information technology (IT) and supporting the distribution of knowledge and information for training and education. Since the COVID-19 pandemic outbreak only few studies have been devoted on studying the impact of cultural characteristics, economic situations, skills and knowledge on the development and the wider human wellbeing. At this study the literature production of economic development was investigated in the light of the ignored, but critically important, issue of globalized inclination to acquire knowledge and skills. All essential aspects of economic systems and economic development within the COVID-19 era were approached, quantified, and graphically valuated, in the light of the following fields of literature search: “dynamic economic systems”, “economic development”, “knowledge skills”, and “globalization”. The measurable indicators of comparing these results were that of: chronological, geographical, languages of reports’ written, subject areas, and keywords, accordingly. Besides, the three domains of technology, environment – ecology, and socio-economics were conveyed, while the key-determinants of knowledge and skills acquisition were also analyzed. From a managerial point of view the simultaneous affection of cultural characteristics, economic simulations, skills and knowledge aspects were considered as positive and significant, thus, supporting managers to better understand the necessities of IT development and managing a highly qualified workforce to shift the thread of the COVID-19 era while performing feasible learning management to solidify workers’ education through formal and in-house education.

scholarly journals Language switching training modulates the neural network of non-linguistic cognitive control

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247100
Author(s):  
Mo Chen ◽  
Fengyang Ma ◽  
Zhaoqi Zhang ◽  
Shuhua Li ◽  
Man Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Control ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Language Switching ◽  
Dorsal Anterior Cingulate Cortex ◽  
The Neural Network ◽  
The Impact

Bilingual language experience, such as switching between languages, has been shown to shape both cognitive and neural mechanisms of non-linguistic cognitive control. However, the neural adaptations induced by language switching remain unclear. Using fMRI, the current study examined the impact of short-term language switching training on the neural network of domain-general cognitive control for unbalanced Chinese-English bilinguals. Effective connectivity maps were constructed by using the extended unified structural equation models (euSEM) within 10 common brain regions involved in both language control and domain-general cognitive control. Results showed that, the dorsal anterior cingulate cortex/pre-supplementary motor area (dACC/pre-SMA) lost connection from the right thalamus after training, suggesting that less neural connectivity was required to complete the same domain-general cognitive control task. These findings not only provide direct evidence for the modulation of language switching training on the neural interaction of domain-general cognitive control, but also have important implications for revealing the potential neurocognitive adaptation effects of specific bilingual language experiences.

scholarly journals Evaluation of VIIRS Neural Network Cloud Detection against Current Operational Cloud Masks

2020 ◽  
Author(s):  
Charles H. White ◽  
Andrew K. Heidinger ◽  
Steven A. Ackerman
Keyword(s):  
Neural Network ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Regional Analysis ◽  
True Positive Rate ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
The Neural Network ◽  
Cloud Mask ◽  
The Impact

Abstract. Cloud properties are critical to our understanding of weather and climate variability, but their estimation from satellite imagers is a nontrivial task. In this work, we aim to improve cloud detection which is the most fundamental cloud property. We use a neural network applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements to determine whether an imager pixel is cloudy or cloud-free. The neural network is trained and evaluated using four years (2016–2019) of coincident measurements between VIIRS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). We successfully address the lack of sun glint in the collocation dataset with a simple semi-supervised learning approach. The results of the neural network are then compared with two operational cloud masks: the MODIS-VIIRS Continuity Cloud Mask (MVCM) and the NOAA Enterprise Cloud Mask (ECM). We find that the neural network outperforms both operational cloud masks in most conditions examined with a few exceptions. The largest improvements we observe occur during the night over snow or ice covered surfaces in the high latitudes. In our analysis, we show that this improvement is not solely due to differences in optical depth-based definitions of a cloud between each mask. We also analyze the differences in true positive rate between day/night and land/water scenes as a function of optical depth. Such differences are a contributor to spatial artifacts in cloud masking and we find that the neural network is the most consistent in cloud detection with respect to optical depth across these conditions. A regional analysis over Greenland illustrates the impact of such differences and shows that they can result in mean cloud fractions with very different spatial and temporal characteristics.

Bolt Tightening Using Impact Wrench Based on Neural Networks

2000 ◽  
Vol 12 (6) ◽  
pp. 706-711
Author(s):  
Toru Fujinaka ◽  
◽  
Hirofumi Nakano ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Clamping Force ◽  
The Neural Network ◽  
The Impact

A method for controlling the tightening operation of bolts using an impact wrench is proposed, where the neural network is employed for achieving proper clamping force. The characteristics of the clamping force depend on the kind of work to which bolts are tightened, thus a neural network is used for classification of the work. The clamping force, which can only be measured during the test run, is estimated online, using another neural network. Then appropriate input to the actuator of the impact wrench is determined, based on the estimated value of the clamping force.

Long-Term Power Load Forecast Based on Improved BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 848-853
Author(s):  
Shuang Chen Li ◽  
Di Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Setup Time ◽  
Time Sequence ◽  
Bp Algorithm ◽  
Load Forecast ◽  
The Neural Network ◽  
Power Load

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

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