Dynamic Neural Network Model Research Articles

A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

ABSTRACTA novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.

Degradation assessment of wind turbine based on additional load measurements

In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model

Wake effect is a significant factor contributing to power loss in wind farms. Studies have shown that wake steering control can mitigate this power loss. Currently, wind farm wake control strategies primarily utilize fixed yaw control due to limitations in the accuracy and efficiency of dynamic wake models. However, fixed yaw control fails to fully exploit the power improvement potential of wake steering control. Therefore, in this study, we first propose a dynamic wake model for wind farms based on the physics-guided neural network (PGNN) approach. This model can predict the dynamic wake flow field within wind farms in real time using instantaneous inflow wind speed and turbine operational states. Then, by employing the PGNN dynamic wake model as the predictive model, a wind farm dynamic wake control strategy based on the model predictive control method is proposed. To quantify the advantages of the proposed control strategy, both fixed yaw control and dynamic yaw control are tested on a wind farm with a 3 × 2 layout. Results from large eddy simulations demonstrate that the proposed dynamic wake control strategy increases the power output of the wind farm by 11.51% compared to a 6.56% increase achieved with fixed yaw control.

Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal.

Fracture mechanism of Ti60 alloy at high temperature in very high cycle fatigue regime and fatigue life prediction

AbstractThis study aimed to investigate the failure mechanism of Ti60 titanium alloy in the high cycle fatigue (HCF) and very high cycle fatigue (VHCF) at 500°C. Ti60 specimens were characterized before and after the 500°C VHCF test, and the fracture surfaces were observed. The results show that there are three different fatigue failure mechanisms at 500°C: (i) equiaxed primary α phase (αp) cleavage, forming small unsmooth facets and rough fracture area (RA), and fusion of non‐adjacent facets to grow into the main crack. (ii) αp grains gather into large grain with triple junction, and large grain slip to form large fusion facet. (iii) Oxide shedding and then cracks form. A dynamic recurrent neural network model is used to predict the fatigue life of Ti60 alloy, 91% of the overall predictions were within the scatter band of 3.0, and 100% were within the scatter band of 5.0.

On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data

This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO2 capture process.

Artificial Intelligence Based Flood and Landslide Disaster Monitoring and Dynamic Numerical Prediction System

Monitoring and dynamic numerical prediction of flood and landslide disasters are of great significance for reducing disaster risks, ensuring urban safety and stable development. The existing systems have limitations in real-time and dynamic performance, making it difficult to provide effective support for disaster prevention and management. To improve the efficiency of disaster monitoring, this article combined artificial intelligence (AI) to conduct in-depth research on the construction of flood and landslide disaster monitoring and dynamic numerical prediction systems. This article first analyzed the system functional requirements, and then designed the system architecture based on this, dividing it into three levels: user interface layer, application layer, and data layer. Finally, the BP (Back Propagation) neural network algorithm and dynamic numerical model were utilized to analyze the monitoring and dynamic numerical prediction of flood and landslide disasters. To verify the effectiveness of the system, this article compared it with GIS (Geographic Information System) based methods, and conducted testing and analysis on the system from monitoring errors, real-time monitoring, numerical simulation, and several levels. The results showed that in terms of real-time monitoring, compared with GIS based disaster prediction systems, the average response time of the AI based disaster monitoring and prediction system in this paper was shortened by 1.36 milliseconds. The conclusion indicated that the AI-based flood and landslide disaster monitoring and dynamic numerical prediction system in this article could effectively improve the efficiency of disaster monitoring and early warning, and help promote the efficient and intelligent development of disaster prevention

Integrated model construction for state of charge estimation in electric vehicle lithium batteries

This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector machines for model optimization is proposed. This strategy involves preprocessing the data, selecting appropriate kernel functions for training, and merging prediction results to enhance the stability of the model. Results indicated that the Dynamic Genetic Kalman Neural Network (DGKNN) model achieved the minimum prediction error percentage of only 0.1529% when the correction coefficient was set to 0.7. The DGKNN model consistently exhibited the lowest error percentage, average absolute error, mean square error, and root mean square error when handling small, medium, and large datasets. For instance, in the small dataset, the error percentage was only 0.1518, and the root mean square error was only 0.0604. The research findings demonstrated that the proposed model exhibited high real-time accuracy in predicting battery SOC, enabling real-time monitoring of battery operating parameters. The method proposed in this study can accurately predict the state of battery charge, extend the life of battery packs, and improve the performance of electric vehicles. It has important significance for promoting the development of the electric vehicle industry.

Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions

In order to produce marketable yield, scientific methodologies must be used to forecast the greenhouse microclimate, which is affected by the surrounding macroclimate and crop management techniques. The MATLAB tool NARX was used in this study to predict the strawberry yield, indoor air temperature, relative humidity, and vapor pressure deficit using input parameters such as indoor air temperature, relative humidity, solar radiation, indoor roof temperature, and indoor relative humidity. The data were normalized to improve the accuracy of the model, which was developed using the Levenberg–Marquardt backpropagation algorithm. The accuracy of the models was determined using various evaluation metrics, such as the coefficient of determination, mean square error, root mean square error, mean absolute deviation, and Nash–Sutcliffe efficiency coefficient. The results showed that the models had a high level of accuracy, with no significant difference between the experimental and predicted values. The VPD model was found to be the most important as it influences crop metabolic activities and its accuracy can be used as an indoor climate control parameter.

NotFYCEX: A Simulation Based Price Prediction and Notification System Using Continuous Machine Learning Method

This research study presents NotFyCEX - a price notification systems model based on Internet of Things (IoT) and Blockchain technology for reporting important trends in a Cryptocurrency Exchange (CRYPTEX) market. The system is simulated in near real time using a co-simulator approach including web-based PHP server-side application for emulating price fluctuations, a dynamic neural network model application integrated within the MATLAB/SIMULINK software environment and ArduinoUNO hardware for continual physical signalling/alerting. Dynamic simulation results showed exact matches between synthesized price state representations and the predicted pattern states. Thus, we consider NotFyCEX a novel effort that can effectively serve as a notification and price prediction system to the end-user or cryptocurrency trader in a given CRYPTEX market.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject's fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.

A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction

A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction

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

Quantitative study of the effect of high-performance zinc alloy coating technology on the life of mechanical components

Abstract Corrosion of mechanical components has been of wide interest in recent decades. Corrosion is caused by physicochemical action between a metal and its environment, which can result in changes in the metal’s properties and functional damage in mechanical components. The main corrosion manifestations of zinc alloy coatings in marine environments are discussed in this paper, which first explores the application of high-performance aluminum alloy coatings in industry. Subsequently, the ZAS35 alloy used for this paper was experimentally prepared, and orthogonal tests were utilized to determine the optimum matching values of process parameters for zinc alloy coatings to generate materials. The hardness and wear resistance of ZAS35 were evaluated against other zinc alloys. An iterative learning control algorithm was employed to determine the thickness of the zinc alloy coating. The optimal control of the steady-state process of coating thickness can be achieved by using the NARX dynamic neural network model as a predictive identification model for zinc alloy coating thickness. Finally, data were collected using an optical microscope to quantitatively analyze the effect of zinc alloy plating on mechanical life. When the thickness of zinc alloy is 1.7 μm, the probability of life is [947.56,978.36]×103h interval t=0.999, which is improved by 409~684.11×103h compared with the plating thickness of 1.10μm . After adding aluminum elements to zinc plating, the corrosion potential of the plating decreases from −800mV to −1000mV, and the zinc-aluminum alloy prevents electrochemical corrosion of the plated layer with cathodic corrosion inhibition.

Local dynamic neural network for quantitative analysis of mixed gases

The Gas sensor array is commonly used in combination with quantitative analysis method for detecting mixed gases. Artificial neural network (ANN) is usually employed to achieve a quantitative analysis of the mixed gases. However, the current ANN models typically require a large number of test samples to obtain low relative errors. In this work, we fabricated a gas sensor array composed of four gas sensors (i.e., In2O3: NO2, Pd-ZnO: NH3, Au-SnO2: CH4, Pd-LaFeO3: CO2) and proposed a local dynamic neural network (LDNN) model for quantitative analysis of four mixed gases. By constructing and extracting features through a pre-trained autoencoder network, only a small sample size (25 local points) is input into the LDNN for training to realize the concentration prediction of four gases. The results show that the MAEs (mean absolute error) of the predicted concentrations of NO2, NH3, CH4, and CO2 are 0.01 ppm, 0.04 ppm, 0.13 ppm, and 42.67 ppm, while the MREs (mean relative error) are 0.19%, 0.85%, 1.17%, and 1.06%, respectively. Moreover, the MREs of the predicted concentrations for four gases are less than 2% within the 95% confidence interval. This work provides an effective quantitative analysis method with small sample size, simple structure and high-precision for the detection of four mixed gases.

Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).

Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions

Both static and dynamic adaptive neural networks have been broadly utilized in mathematical modeling and numerical analysis. This study aimed to enhance the accomplishment of Dynamic Neural Networks (DNN) models by applying wavelet functions as activation functions. Research that models and forecasts the intensity of solar radiation in Mataram City shows that combining B-Spline and Morlet wavelet activation functions can significantly increase the DNN model performance. Wavelet-DNN (W-DNN) was modeled with an identical architecture; the best showed the increase in the model achievement (0.7596 points for in-sample and 0.8502 points for out-sample data). Mainly for out-sample data, the model's performance using the W-DNN+ intervention model increased by 4.0492 points.

Multi-relational dynamic graph representation learning

In recent years, many dynamic graph representation learning methods have emerged due to the ubiquity of dynamic graph networks, such as social networks, medical networks, citation networks and traffic networks. Many researchers only consider the unitary relational topological information in a dynamic graph. However, real dynamic networks contain a large amount of multi-relational topological information. For example, there are different interactive relations such as sending a message, adding a friend, making a phone call, and sending an email in a social network, and they have different effects on node representation and should be distinguished. In addition, the non-topological information of nodes plays an important role in the node representation. Although these two types of information have been shown to improve the performance of many dynamic graph tasks, existing dynamic graph representation learning models could not integrate them well. Therefore, in this paper, we propose MRDGNN, a Multi-Relational Dynamic Graph Neural Network model, which can capture the dynamic evolution under each relational topology in the graph through a temporal multi-relational topology updater, including the participation of multi-relational topological and non-topological information of nodes. These two kinds of information will be adaptively fused into the representation of nodes by a merger. MRDGNN is continuously updated with the evolution of dynamic graphs and is a real-time learnable representation learning framework. Finally, we validate the effectiveness of MRDGNN for link prediction and relation prediction on four real datasets.

Dynamic particle swarm optimization-radial function extremum neural network method of HCF probability analysis for compressor blade

The high cycle fatigue (HCF) of compressor rotor seriously affects the performance and reliability of gas turbine. Probabilistic HCF evaluation is an effective measure to quantify the uncertain traits of vibration stresses and assess the reliable HCF life for compressor rotor. To improve modeling accuracy and computational efficiency in transient probability analysis of HCF life of gas turbine compressor, radial basis function neural network (RBFNN) and dynamic particle swarm optimization (DPSO) algorithm were introduced into extreme response surface method (ERSM). In this paper, we propose a HCF life probability evaluation method for compressor blades based on the dynamic particle swarm optimization-radial basis function extremum neural network (DPSO-RBFENN) model. By selecting random input variables such as aerodynamic load, centrifugal load and material parameters, a prediction model was established based on DPSO-RBFENN sample learning, and the reliability evaluation of compressor blade HCF life was carried out. Distribution characteristics, reliability and sensitivity to fatigue life were obtained, which provided a basis for the structural design of compressor blades. The Monte Carlo method, ERSM, RBFNN and DPSO-RBFENN are compared.