Dynamic Artificial Neural Network Research Articles

Optimizing stock market volatility predictions based on the SMVF-ANP approach

The stock market is considered one of the most complicated financial systems, comprising several components or inventories, whose prices vary substantially over time. Bursaries include revealing market tendencies over time. All investors in the stock market aim to maximize profits and reduce the associated risks. Stock currency forecasts are a significant financial concern that is being handled even more closely. In recent years, various neural network and hybrid models have surpassed classic linear and non-linear techniques to produce reliable prediction outcomes. This study investigates the efficacy of dynamic and effective stock market forecasting using neural network models. This study examines market transmission mechanisms and assesses the predicted links between multiple financial and economic factors. A stock market volatility and artificial network prediction (SMVF–ANP) approach is presented. The models analyzed included a multi-layer perceptron (MLP), dynamic artificial neural network, and generalized autoregressive conditional heteroscedasticity to extract additional input variables. The results reveal that the trade strategies led by the classification models yield superior risk-adjusted returns compared to the buy-and-hold approach and those led by the neural network and linear regression model-level estimate predictions. The numerical results show that the proposed SMVF-ANP method achieved a high performance ratio of 94.1%, an enhanced prediction ratio of 98.4%, a high stock market volatility rate of 96.7%, a reduced mean square percentage error ratio of 16.3%, a probability rate of 32.7%, an increased efficiency rate of 96.9%, and an accuracy ratio of 97.2% compared to other methods.

CFD‐ANN coupling model simulation of gas–solid feeding design for ternary biomass mixtures in bubbling fluidized bed gasifier

AbstractThe performance of continuous feeding fluidized bed reactors is significantly influenced by their design. These reactors can effectively operate with a wide range of biomass mixtures. Therefore, it is imperative to carefully design the gas distributor plate and solid tube inlet to ensure stable fluidization and uniform distribution of fluidizing gas and solid particles within the reactor. This study investigated the impact of gas–solid feeder design in bubbling fluidized bed gasifier for biomass mixtures on system hydrodynamics, employing a computational fluid dynamics–artificial neural network (CFD‐ANN) coupling model to achieve more realistic simulations. A 2k factorial experimental design was adopted to inquire the impact of gas and solid feeding systems. The responses under investigation included the gas–solid mixing index and the solid residence time, both of which hold pivotal roles in specific chemical processes related to biomass utilization in fluidized bed technology. All cases were successfully simulated, and the results uncovered that the position and length of the solid inlet tube wielded a significant influence on reactor performance, particularly concerning solid residence time. Furthermore, the designs of the gas distributor were identified as critical factors capable of enhancing system turbulence and mixing. In summary, the results showed the potential for enhancing reactor performance through the optimization of gas–solid feeding systems and underscored the efficacy of the ANN drag model in simulating continuous biomass gasification systems.

Using social networking evidence to examine the impact of environmental factors on social Followings: An innovative Machine learning method

Social media platforms have changed the way of communication. They are making the world more connected and provide information more quickly, which has resulted in beneficial and harmful effects on society. Analyzing data from multiple social media platforms to spot patterns and trends in user behavior in response to environmental changes is part of using social networking evidence to investigate the effects of environmental variables.The infinite collection of personal data generates privacy issues and ethical questions about data security and utilization.We present a novel framework called meta-heuristic atom search optimized dynamic artificial neural network (MASO-DANN) method for examining the impact of environmental factors using social networking evidence and the complementary abilities between MASO as well as DANN. The study aims to determine the connections between the dynamics of users' social relationships and involvement by examining the data from various online social platforms. We collected a Twitter dataset and preprocessed the data using the stop word removal preprocessing method.With the use of MASO-DANN, extensive social networking data analysis has been rendered achievable, which offers an exhaustive overview of the complex dynamics influencing online communities. The result findings examined various metrics, such as accuracy (95.07%), precision (89%), recall (88%), F1-score (90%) and RMSE (1.6).These metrics demonstrate the robustness along with the dependability of MASO-DANN and its capacity to provide more accurate predictions that evaluate the effects of environmental factors based on social networking evidence.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Event-Triggered $H_{\infty}$ Tracking Control for Dynamic Artificial Neural Network Models With Time-Varying Delays

Event-Triggered $H_{\infty}$ Tracking Control for Dynamic Artificial Neural Network Models With Time-Varying Delays

Prediction of Financial Distress Using Dynamic Artificial Neural Network for Early Warning System

Prediction of Financial Distress Using Dynamic Artificial Neural Network for Early Warning System

Dynamic artificial neural network model for ultralow temperature prediction in hydrogen storage tank

Hydrogen fuel cell vehicles are gaining popularity as an alternative to electric vehicles. To support their use, research on hydrogen-storage devices and charging stations is being conducted actively. However, high-pressure hydrogen dispensing can lead to a temperature increase and potential explosion hazards. Therefore, maintaining safe hydrogen temperatures during charging is crucial. However, accurately predicting temperature changes in hydrogen heat exchangers is challenging owing to rapidly changing operating conditions and large temperature differences. In this study, we adopt a deep-learning-based artificial neural network for predicting temporal temperature variations in low-temperature hydrogen heat exchangers. Two networks, namely, the deep neural network (DNN) and recurrent neural network (RNN), are constructed with Bayesian optimization based on the experimentally measured data from an ultralow-temperature hydrogen heat exchanger to predict temperature. Both models showed superior prediction accuracies of temperature within 3.11 % of mean absolute error (MAE) and heat transfer rates of the heat exchanger within 2.53 % of MAE. In particular, DNN modeling is recommended as a universal method for predicting the temperature when previous data are known, and RNN modeling is recommended for predicting the temperature for successive actions in a compressed-hydrogen system.

Gas Turbine Vibration Detection and Identification based on Dynamic Artificial Neural Networks

Vibration control in rotating machinery is a major challenge in oil and gas facilities that use these machines. In gas turbines, the instability phenomenon is generated by the rotor, and measurements must be made in the axial plane of the turbine. Minor defects can lead to significant vibration amplifications, making it imperative to detect these defects early. The goal of this study is to develop a diagnostic strategy to monitor faults affecting a turbine system using a supervision approach based on artificial neural networks. This strategy allows for early detection of faults, which allows for efficient management of vibration-induced failures, as well as economic gain, by recovering the transported gas used in these machines. By describing the vibration-related parameters and representing the state of the vibratory motion, the proposed approach provides a powerful tool for vibration control in rotating machines.

Investigation on the quantitative evaluation method of coal combustion situation in O2-CO2-N2 atmospheres based on dynamic artificial neural network

ABSTRACT Due to the semi-enclosed space of underground coal seams and the implementation of nitrogen injection measures, most coalfield fires occur in multi-atmosphere environments. At present, there is little research on the combustion situations and kinetics of coal under multi-atmosphere conditions. In this paper, simultaneous thermal analysis experiments were conducted to investigate the combustion behaviors of bituminous coal and anthracite in O2-CO2-N2 atmospheres with different volume fractions. A new index for quantitative characterization of coal combustion situation in multi-gas mixing atmospheres was proposed based on the combustion situation intensity index and heat release rate. Furthermore, a method to predict the coal combustion situation using artificial neural network (ANN) was proposed. The results showed that the combustion rate decreased with the increase in CO2 volume fraction and the decrease of N2 volume fraction under multi-gas mixing atmospheres. The volume fraction changes in inert gases had little effect on the ignition temperature or burnout temperature and had a greater effect on the combustion reactivity of bituminous coal. In a 15% O2-5% CO2-80% N2 atmosphere, the combustion could be inhibited for bituminous coal, while being promoted by anthracite. The combustion situation of bituminous coal in a 15% O2-25% CO2-60% N2 atmosphere was greater than that in air. Based on the proposed coal combustion situation index and the selected optimal ANN model, the coal combustion situation in O2-CO2-N2 atmospheres could be well predicted. This paper provides a reliable theoretical basis for judging the coal combustion spread in the coalfield fire area.

Time-delay neural network modeling of the running-in wear process

A time-delay neural network (TDNN) model is developed for describing the variation of the wear rate during the running-in wear process under constant relative sliding velocity. Besides the current value of the normal load, the neural network inputs also include its several preceding values. A specific feature of the TDNN model is that, by analogy with the Archard–Kragelsky wear equation, the neural network incorporates a power-law activation in the output layer for the current load input, which is directly fed into the output neuron along with the outputs of the hidden neurons. A motivation for the choice of such a dynamic artificial neural network model is given, and an intuitive explanation for the TDNN operation is suggested. A predictive capability of the TDNN model is illustrated based on experimental data taken from the literature.

Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data

Real-time and accurate travel time information of transit vehicles is valuable as it allows passengers to plan their trips to minimize waiting times. The objective of this research was to develop a dynamic artificial neural network (ANN) model that can provide accurate prediction of bus travel times to give real-time information at a given downstream bus stop using only global positioning system (GPS) data. The ANN model is trained off-line but can be used to provide real-time travel time information. To achieve this, care was taken in selecting a unique set of input-output combinations for prediction. The results obtained from the case study are promising to implement an Advanced Public Transportation System (APTS). The performance of the proposed ANN model was compared with a historical average model under two criteria: prediction accuracy and robustness. It was shown that the ANN outperformed the average approach in both aspects.

Dynamic state feedback controller and observer design for dynamic artificial neural network models

Artificial neural networks are black-box models that can be used to model nonlinear dynamical systems. This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural networks. The resulting control designs are applicable to the practical situation in which the steady-state values for the control input are not known. Dynamic artificial neural networks are written in the standard nonlinear operator form, also known in the literature as the Luré formulation. A generalized form of the Luré formulation is adopted to allow for the representation of deep ℓ-layer networks, ℓ≥1. Sufficient conditions for controller synthesis and observer design are derived in the form of linear matrix inequalities, using a quadratic Lyapunov function. The synthesis method is demonstrated for the control of pH in two tanks in series and a numerical example.

The Application of A Combined Computational Fluid Dynamics (CFD) Artificial Neural Network (ANN) to Increase The Prediction Accuracy of Sediment Grading in Subsea Pipes: A Literature Review

In recent years, the implementation of subsea pipelines for oil and gas transportation has increased. One of the important aspects of the design process of the subsea pipeline is scour prediction. Scouring causes the subsea pipeline to lose its support and is susceptible to failure due to deflection. This paper presents the result of a literature review of scouring-related research to obtain a method to increase scouring prediction accuracy. Based on the literature research, it is known that the errors found in Computational Fluid Dynamics (CFD) are mainly affected by the flow models. Existing flow models cannot fully represent the complexity of turbulent flow that occurs during the scouring process. Artificial Neural Network (ANN) can reduce the error value. But, the CFD-ANN hybrid methods can potentially decrease the error value by about 20% more than CFD. Therefore, the CFD-ANN hybrid method is expected to be a new method that could be used to predict subsea pipeline scouring in the oil and gas industry.

Data driven identification of industrial reverse osmosis membrane process

A dynamic artificial neural network (ANN) is developed for the identification of an industrial reverse osmosis membrane process under fouling effect. 4-year historical data on feed properties and measured process variables in the plant were used for the ANN training and validation. The ANN considers the current and previous week's online measurements as inputs and provides one-week and two-week ahead permeate flow predictions. A sensitivity analysis is provided at various periods to determine the variables with high impact on the permeate flow, and thus the plant performance. Based on the sensitivity analysis, cartridge filter pressures and pH have the highest impact on the output. Plant operating window is also calculated under such complex multivariable and nonlinear nature. The results show quantitative and intuitive conclusions, parallel to existing literature, and provide significant insight on the identification of industrial and large-scale reverse osmosis processes.

Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting

In this paper a dynamic Artificial Neural Network (ANN) model called Meta-ANN is developed for forecasting the short-term grid load. The primary ingredient of the model is a base module which is an ANN trained over a large historical data set to learn the long-term trend and seasonality of grid load. To capture the nonstationary pattern of the grid load, an error-correction module based on the idea of meta-learning is integrated into the model. This module finetunes the base module according to most recent prediction errors. For each day of interest, Meta-ANN generates a new ANN model started from the base module by tracing the gradient of the prediction loss on recent observations weighted by learning rates with specific structures. The full Meta-ANN model is trained by jointly optimizing the base module and error-correction module via gradient descent algorithms. The implementation based on gradient descent algorithms is detailed with streamlined mathematical formulations. The proposed model is tested on the open-access data from Elia, a Belgian transmission system operator, for forecasting the daily mean load and load profile. The numerical study shows that Meta-ANN makes more accurate and robust prediction by effectively capturing the nonstationary pattern in grid loads.

Compact neural network modeling of nonlinear dynamical systems via the standard nonlinear operator form

Dynamic artificial neural networks (DANNs) have become popular for the data-driven modelling of nonlinear dynamical systems. This article elucidates properties of a compact DANN model structure called the standard normal operator form (SNOF). Sets of nonlinear dynamical systems are characterized for which the SNOF can achieve the same model identification error with fewer neurons than needed by the popular DANN model structures. The results are demonstrated in a case study for a multi-stage bioreactor, which is a highly nonlinear dynamical system in which the cells have a sharp change in dynamics during a change in the feed composition as the bioreactor shifts from growth mode to production mode. The ability of the SNOF to model highly nonlinear dynamical systems with a very small number of neurons suggests its potential for serving as a basis for the design of model-based optimal control systems with theoretical guarantees of closed-loop stability and performance.

Investigation on the Quantitative Evaluation Method of Coal Combustion Intensity in O2-Co2-N2 Atmospheres Based on Dynamic Artificial Neural Network

Investigation on the Quantitative Evaluation Method of Coal Combustion Intensity in O2-Co2-N2 Atmospheres Based on Dynamic Artificial Neural Network

Investigation on the Quantitative Evaluation Method of Coal Combustion Intensity in O2-Co2-N2 Atmospheres Based on Dynamic Artificial Neural Network

Investigation on the Quantitative Evaluation Method of Coal Combustion Intensity in O2-Co2-N2 Atmospheres Based on Dynamic Artificial Neural Network

Reactive Column to Separate Ethyl Acetate-Ethanol-Water Using Fuzzy Dynamic Artificial Neural Networks

Reactive Column to Separate Ethyl Acetate-Ethanol-Water Using Fuzzy Dynamic Artificial Neural Networks

Prediction of time-varying inner wall temperature of surge lines by a dynamic neural network

Thermal fatigue, mainly caused by thermal stratification, is one of the major factors for reliability degradation of surge lines in nuclear power plants and is well managed through on-line temperature monitoring. However, the vast amount of outer wall temperature data generated by on-line temperature monitoring requires an efficient way of calculating the inner wall temperature. In this paper, the time-varying inner wall temperature is predicted rapidly and precisely using a dynamic artificial neural network model based on outer wall temperature and flow velocity. By optimizing the length of time series, the time delay of heat transfer between the inner wall and outer wall temperature is fully considered in the dynamic model. Consequently, the best root mean squared error of the temperature prediction is as low as 3.78. More importantly, the importance analysis based on dynamic model is also performed to quantify the importance of outer wall temperature variables, which can be used to optimize the arrangement of outer wall temperature measurement points for further applications under different circumstances to reduce cost while maintain high prediction accuracy. The results in this study will be helpful for the optimization of measurement points arrangement and rapid on-line thermal fatigue evaluation of nuclear power plants.