Wavelet Neural Network Algorithm Research Articles

Application of energy scheduling algorithm based on energy consumption prediction in smart home energy scheduling

To effectively optimize household energy management, enhance user comfort, and ensure the stability of the power grid, this paper directs its focus towards household equipment and presents inter-household and indoor energy scheduling algorithms. Grounded in a two-dimensional dynamic device priority framework and predictive device operation modeling, this study demonstrates that the utilization of scheduling algorithms can avert grid overload scenarios during periods of low electricity prices. This results in more consistent hourly electricity consumption and a substantial peak-to-average ratio reduction of up to 44.86 %. Additionally, the application of the continuous wavelet neural network algorithm proves to be proficient in accurately forecasting home device operations. When the time was 17.5 h, the actual energy consumption of the water heater was 7.12 kWh, which was 0.78 kWh more than the predicted energy consumption. Utilizing scheduling algorithms, as opposed to not employing them, led to notable reductions in electricity expenses, demonstrating a cost savings rate of 15.32 % on the third day. Additionally, it facilitated decreased usage of the power grid. When the time was 18 o'clock, the power consumption of the grid using scheduling algorithms was 0.24 kWh, which was 0.25 kWh lower than that without scheduling algorithms. Following the implementation of the scheduling algorithm, minimal deviation in device startup times was observed during periods of high comfort, with offsets ranging from 0 h. The research methods employed demonstrate efficacy in orchestrating energy distribution among households and indoor spaces, thereby striking a harmonious balance between energy conservation and user comfort.

Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak–peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.

Research and Application of Coupled Mechanism and Data-Driven Prediction of Blast Furnace Permeability Index

In order to ensure the stable operation of blast furnace production, it is necessary to keep abreast of the trends in the gas permeability index of the blast furnace. As one of the key parameters to be monitored in the process of blast furnace smelting, the gas permeability index directly reflects the performance of the blast furnace in the actual production of the furnace. Continuous monitoring of the permeability index is required in the actual production of the blast furnace in order to effectively guarantee the stable and smooth operation of the blast furnace. The aim of this study is to accurately predict the trend in the blast furnace gas permeability index by constructing an intelligent prediction model and utilizing a data-driven approach to monitor the gas permeability index and ensure the stable operation of the blast furnace. First, based on the actual production data of a #2 blast furnace of an iron and steel enterprise, an isolated forest algorithm is applied to detect and eliminate the outliers in the original data, and then a data driver set is constructed after normalization of the deviation. Second, by analyzing the coupling mechanism between the blast furnace permeability and gas flow, as well as Spearman correlation analysis and MIC maximum information coefficient (MIC) analysis, key parameters are screened out as feature variables from the data-driven set. Finally, a wavelet neural network algorithm is used to construct an intelligent prediction model of the blast furnace gas permeability index. Compared with a BP neural network (BP), a particle swarm-optimized BP neural network (PSO-BP), and XGBoost, the wavelet neural network shows obvious advantages when the error is controlled in the range of ±0.1, and the prediction accuracy can reach 95.71%. The model is applied to the actual production of a #2 blast furnace of an iron and steel enterprise, and the results show that the predicted value of the blast furnace permeability index is highly consistent with the actual value of real-time blast furnace production, which verifies its excellent characteristics.

A novel growing wavelet neural network algorithm for solving chemotaxis systems with blow‐up

In this study, we introduce a new growing neural network algorithm that is based on wavelet neural networks and call our algorithm a growing wavelet neural network (GWNN) method. We apply our proposed scheme to train a wavelet neural network to solve chemotaxis problems with blow‐up. These problems are highly nonlinear time‐dependent systems of partial differential equations, and it is a challenge to get the pattern of the solution accurately. The proposed structure is partial retraining of the network, which increases its capacity to catch the spiky pattern of the solution. Our neural network‐based algorithm allows us to solve the nonlinear chemotaxis problems without the use of linearization techniques and regularization techniques, most of which reduce the accuracy of the model. This mesh‐free‐based method can manage a variety of blow‐up models with curved boundaries without imposing an extra cost. By proving the consistency and stability of the method, we show the convergence of GWNN solutions to analytical solutions of the chemotaxis problem. Several illustrative examples and simulation results are provided to demonstrate the correctness of the results and the robust performance of the presented algorithm. Moreover, to illustrate the effectiveness of the GWNN method, we make a comparison with two other network‐based methods.

Intelligent Energy Management System Evaluation of Hybrid Electric Vehicle Based on Recurrent Wavelet Neural Network and PSO Algorithm

Intelligent Energy Management System Evaluation of Hybrid Electric Vehicle Based on Recurrent Wavelet Neural Network and PSO Algorithm

Reliability Prediction of the Distribution Network Based on Wavelet Neural Network with Quantum Particle Swarm Optimization Algorithm

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks.

Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN

Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines.

Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders

Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

According to the definition of nonlinear cointegration, this article studies the small sample nonlinear cointegration test and NECM (Nonlinear Error Correction Model) based on the LS‐SVM (Least Squares Support Vector Machine) optimized by PSO (Particle Swarm Optimization). And the logical process of this method is also designed. Then, we carry out empirical research on the ship maintenance cost index and the several price indexes. Based on the judgment of the type of cointegration test relationship, the test of the nonlinear cointegration test relationship of small samples is realized, and the NECM for predicting the ship maintenance cost index is established as well, which is compared with the VAR model of linear vector. The research result shows that the small sample nonlinear cointegration test and modeling method based on the LS‐SVM Optimized by PSO can describe the nonlinear cointegration test relationship of the small sample system. And the NECM has better performance. The prediction effect can effectively predict small sample nonlinear systems. We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS‐SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

BackgroundEstimation of nitrate nitrogen (NO3−–N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3−–N contents in cotton petioles under drip irrigation is of great significance.MethodsIn this study, we discussed the use of hyperspectral data to estimate NO3−–N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3−–N contents were first investigated, after which a traditional regression model for petioles NO3−–N content was established. A wavelet neural network (WNN) model for estimating petiole NO3−–N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP).ResultsBetween the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3−–N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP.ConclusionsAn inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3−–N content estimation in cotton petioles under drip irrigation.

Fault detection in rotor system by discrete wavelet neural network algorithm

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

Research on UUV Recovery Active Disturbance Rejection Control Based on LMNN Compensation

As a kind of intelligent marine equipment, its successful recovery is the basis of Unmanned Underwater Vehicle (UUV)’s normal working and completing the mission successfully. However, the mathematical model of UUV is severely coupled and highly nonlinear, and may be subject to complex interference in the recovery process. Besides, the model could not be determined completely. Active disturbance rejection control (ADRC) method does not need the beforehand information of the unknown disturbance and also can ensure the stability. But conventional ADRC method with fixed parameters could not adjust to UUV complicated motion control. This paper introduces the adaptive wavelet neural network optimized by Levenberg-Marquardt algorithm, and puts forward a novel ADRC control method improved by wavelet neural network algorithm and Levenberg-Marquardt algorithm, for partial system identification and uncertain model compensation. Moreover, with pitching moment change considered in the process of UUV recovery, two dimensionless hydrodynamic coefficients are defined based on near-wall effect. The simulation experiments have been tested to verify the effectiveness of the proposed control. The results indicate that the ADRC with Levenberg-Marquardt neural network could control UUV recovery process in the variable disturbance environment more stable and reduce the ADRC computational burden.

Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm.

Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

Compensation of Geometric Parameter Errors for Terrestrial Laser Scanner by Integrating Intensity Correction

The accuracy of geometric parameters (mainly referred to the incidence angle and measuring distance) in a terrestrial laser scanner (TLS) is not only influenced by the TLS intrinsic systematic instrumental error but also the extrinsic received intensity data. However, the current error compensation methods for geometric parameters mainly focus on the calibration of TLS intrinsic systematic instrumental error and rarely consider the extrinsic intensity data correction. For this reason, this article presents a new method integrating the TLS intrinsic systematic instrumental error calibration and extrinsic intensity data correction to compensate the TLS geometric parameter error. The error compensation procedure is implemented as follows. First, the error compensation mathematical model integrated with TLS intrinsic systematic instrumental error calibration parameters and extrinsic intensity data correction coefficient is established. Second, the hybrid harmonic analysis (HA) and the adaptive wavelet neural network (AWNN) algorithm are proposed to calculate the TLS incidence angle error compensation values. Subsequently, the cubic spline interpolation (CSI) is applied to compute the measuring distance error compensate values. Finally, the TLS (model FARO Focus S150) and the hemispherical angle calibration instrument were used to evaluate the proposed compensation method. The experimental results demonstrate that the geometric parameters are significantly influenced by the intensity data received from TLS, and the proposed method can effectively improve the overall accuracy of the TLS incidence angle and measuring distance.

Time series modeling of ionosphere total electron content using wavelet neural network and hybrid PSO training algorithm

در این مقاله از ترکیب شبکه‌های عصبی موجک سه‌لایه (WNNs) به همراه الگوریتم آموزش بروش بهنیه‌سازی انبوه ذرات هیبرید (PSO-BP) جهت مدل‌سازی تغییرات زمانی-مکانی محتوای الکترون کلی (TEC) یونوسفر در منطقه شمالغرب ایران (N-W) استفاده شده است. مشاهدات 30 روز ماه ژانویه سال 2018 جهت ارزیابی روش پیشنهادی بکار گرفته شده است. تعداد مشاهدات ورودی انتخاب شده جهت آموزش شبکه عصبی موجک سه‌لایه با الگوریتم آموزش PSO-BP بترتیب 20 و 10 ایستگاه از شبکه محلی آذربایجان می‌باشند. در هر 2 حالت تعداد 3 ایستگاه با توزیع مناسب به عنوان ایستگاه‌های آزمون در نظر گرفته شده‌اند. شاخص‌های آماری خطای نسبی، خطای |dVTEC|، انحراف معیار و ضریب همبستگی جهت ارزیابی مدل شبکه عصبی موجک مورد استفاده قرار گرفته است. نتایج حاصل از مدل پیشنهادی این مقاله با TEC حاصل از مشاهدات GPS به عنوان مرجع اصلی، مدل مرجع جهانی یونوسفر 2016 (IRI2016) و همچنین خروجی شبکه جهانی IGS (GIM) مقایسه شده است.

Modeling and Simulation of Human Muscle Tissue Development in Biomechanics Based on Wavelet Neural Network Algorithm

Modeling and Simulation of Human Muscle Tissue Development in Biomechanics Based on Wavelet Neural Network Algorithm

Robust adaptive backstepping active control of compressor surge based on wavelet neural network

Surge active control can extend the stable operating range of the compressor. Most of the previous active control algorithms can only work on the basis that compression characteristics are known, which limits the engineering application of it. The robust control can alleviate the negative effects brought by uncertain system characteristics, however its designed control law is too conservative that active control can't give full play to its benefits. To overcome the weakness of the former active controllers, this paper comes up with the idea of active surge control, which assumes that the characteristics of the compression system are unknown. And it uses the wavelet neural network algorithm, a kind of algorithm with the nature of approaching any linear and nonlinear functions, to approach the unknown compressor steady-state characteristics asymptotically. Then, by applying backstepping on the second-order MG compressor surge dynamic model with CCV, a robust adaptive active controller of compressor surge is designed, which makes the control law effective to a larger extent, and greatly improve its performance in tracking and anti-interference. From the simulation results, it can be seen that the designed controller displays the strong robustness against un-modeled dynamics, flow and pressure disturbances, and in the meanwhile achieves near to zero asymptotical tracking error. The method proposed in this paper allows compressors to operate stably with high-pressure ratio and high-efficiency beyond the surge boundary, namely greatly expands its operating range. Thus, this paper has significant guidance for future development, theoretical research, and engineering application of surge active control of the compressor.

Prediction of the antifreeze of the concrete structure based on random forest and wavelet neural network

The durability of concrete is the focus of research in the field of engineering, and the resistance of concrete is one of the important indicators of concrete durability. Based on the prediction of early resistance to concrete based on the random forest combined with the wavelet neural network algorithm, 12 factors affecting concrete antifreeze were selected from the ratio level of concrete material, and the relative dynamic elastic mode was used as the evaluation index of concrete resistance, and the importance evaluation and feature variable selection of influencing factors were used by random forest, and a concrete antifreeze prediction model based on RF-WNN (random forest-wave neural network) was established. The example analysis is carried out by taking the project project as an example, and the calculation of the wavelet neural network and BP neural network prediction model without the influence factor screening is compared to obtain the mean square root error and the fit superiority respectively. The results show that the prediction results of RF-WNN model are closer to the actual value and the prediction accuracy is higher, and the proposed RF-WNN prediction model provides an effective method for achieving concrete antifreeze prediction.

Modified Stackelberg Games Approach for Dynamic Signal Control and Route Choice Equilibrium on Mixed Networks

The dynamic signal control and route choice equilibrium are usually integrated into a noncooperative game between the network authority and the road users. There are mainly two problems in most existing optimization methods. Firstly, the authority is often placed in the upper level in bi-level programming models, this pure system-optimization-oriented framework may increase the difficulty in obtaining an equilibrium flow distribution. Secondly, the rate of drivers’ compliance on the control strategy has not been fully investigated, which makes the problem intractable in real time, especially in a connected vehicle (CV) environment. This paper proposes a modified Stackelberg games model to change the format of the authority-user and user-authority dynamically. The direct communication between the authority and users is established, and the drivers’ compliance rate is applied as the level-change threshold index. Considering the difference between the drivers’ realized travel time and the predicted travel time on the variable message sign (VMS), a logit model is formed to calibrate the compliance rate in every time step. Based on a modified wavelet neural network algorithm, the model predictive control (MPC) fulfills the level-change procedure using the software Matlab 2018b. Six benchmarks are applied in a numerical example. The results show that the proposed model with the centralized framework obtains the minimum total travel cost compared with the benchmarks. Combined with the real-time mutual feedback between drivers’ response and control strategy, the level-change procedure potentially maintains the compliance rate within a certain level.