Neural Network and Perturbation Theory Hybrid Models for Eigenvalue Prediction

1999 ◽  
Vol 132 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Michael G. Lysenko ◽  
Hing-Ip Wong ◽  
G. Ivan Maldonado
Keyword(s):  
Neural Network ◽  
Perturbation Theory ◽  
Hybrid Models

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

Adaptive Linearizing Control with Neural-Network-Based Hybrid Models

2001 ◽  
Vol 40 (23) ◽  
pp. 5604-5620 ◽  
Author(s):  
Mohamed Azlan Hussain ◽  
Pei Yee Ho ◽  
J. C. Allwright
Keyword(s):  
Neural Network ◽  
Hybrid Models

scholarly journals Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data

2020 ◽  
Vol 7 (02) ◽  
pp. 1 ◽  
Author(s):  
Rahul Paul ◽  
Matthew B. Schabath ◽  
Robert Gillies ◽  
Lawrence O. Hall ◽  
Dmitry B. Goldgof
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Data ◽  
Lung Nodule ◽  
Hybrid Models ◽  
Neural Network Ensembles ◽  
Network Ensembles

scholarly journals Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

2020 ◽  
Author(s):  
Hossein Safarzadeh ◽  
Marco Leonesio ◽  
Giacomo Bianchi ◽  
Michele Monno
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Regression Model ◽  
Hybrid Models ◽  
Physical Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Grinding Wheel ◽  
Wheel Wear ◽  
Learning Techniques

AbstractThis work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

2019 ◽  
Vol 28 (05) ◽  
pp. 1950017 ◽  
Author(s):  
Guotai Chi ◽  
Mohammad Shamsu Uddin ◽  
Mohammad Zoynul Abedin ◽  
Kunpeng Yuan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Logistic Regression ◽  
Credit Risk ◽  
Risk Prediction ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Hybrid Models ◽  
Multilayer Perceptrons ◽  
Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

scholarly journals Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

2020 ◽  
Vol 10 (15) ◽  
pp. 5160
Author(s):  
Saad Sh. Sammen ◽  
Mohammad Ali Ghorbani ◽  
Anurag Malik ◽  
Yazid Tikhamarine ◽  
Mohammad AmirRahmani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Absolute Error ◽  
Hybrid Models ◽  
Percentage Error ◽  
Scour Depth ◽  
Box Plots ◽  
Taylor Diagram ◽  
Artificial Neural

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

scholarly journals A PERTURBATIVE APPROACH TO NONLINEARITIES IN THE INFORMATION CARRIED BY A TWO LAYER NEURAL NETWORK

2001 ◽  
Vol 15 (03) ◽  
pp. 281-295 ◽  
Author(s):  
E. KORUTCHEVA ◽  
V. DEL PRETE ◽  
J.-P. NADAL
Keyword(s):  
Neural Network ◽  
Perturbation Theory ◽  
Mutual Information ◽  
Nonlinear Term ◽  
Exact Expression ◽  
Neural Systems ◽  
Neuronal Dynamics ◽  
Perturbative Approach ◽  
First Order ◽  
Nonlinear Analogue

We evaluate the mutual information between the input and the output of a two layer network in the case of a noisy and nonlinear analogue channel. In the case where the nonlinearity is small with respect to the variability in the noise, we derive an exact expression for the contribution to the mutual information given by the nonlinear term in first order of perturbation theory. Finally we show how the calculation can be simplified by means of a diagrammatic expansion. Our results suggest that the use of perturbation theories applied to neural systems might give an insight on the contribution of nonlinearities to the information transmission and in general to the neuronal dynamics.

Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models

2020 ◽  
pp. 19-41
Author(s):  
Görkem Sarıyer ◽  
Ceren Öcal Taşar
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Hybrid Models ◽  
Emergency Department Visits ◽  
Daily Number ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Ed Visits ◽  
Day Of The Week ◽  
Input Variables

In this study, linear regression and neural network-based hybrid models are developed for modelling the daily ED visits. Month and week of the year, day of the week, and period of the day, are used as input variables of the linear regression model. Generated forecasts and the residuals are further processed through a multilayer perceptron model to improve the performance of forecasting. To obtain forecasts for daily number of patient visits, aggregation is used where the obtained periodical forecasts are summed up. By comparing the performances of models in generating periodical and daily forecasts, this chapter not only shows that hybrid model improves the forecasting performance significantly, but also aggregation fits well in practice.

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

2021 ◽  
pp. 1-19
Author(s):  
GÖRKEM ATAMAN ◽  
SERPIL KAHRAMAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Stock Market ◽  
Stock Markets ◽  
Hybrid Models ◽  
Stock Market Prediction ◽  
Macroeconomic Variables ◽  
Brics Countries ◽  
Artificial Neural

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

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