Wavelet Neural Network Model Research Articles

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper innovatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in conventional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for personal optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to modify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model has a significant improvement in prediction accuracy and operating efficiency compared with artificial neural network (ANN), WNN, and PSO-WNN.

Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy

Determination of the histamine level in fish is essential not only because it is an indicator of fish freshness but also because this prevents the risk of histamine intoxication in consumers. This study used the strategy of near-infrared (NIR) spectroscopy coupled with a hybrid variable selection for rapid and nondestructive assessment of the histamine level in mackerel. To effectively identify the highly informative spectral variables, a three-step hybrid strategy, combining backward interval partial least squares, selectivity ratio and flower pollination algorithm, was developed. The optimized variables were fitted to the multivariate calibration models of partial least squares model (PLS), radial basis function neural network (RBFNN), and wavelet neural network (WNN). The best model was obtained by the optimized WNN model using the hybrid variable selection method, with R-squared (RP2) value and root mean squared error for prediction were, 0.79 and 70 mg/kg for flesh side dataset, and 0.76 and 75 mg/kg for skin side dataset. The obtained results for the skin side dataset significantly outperformed the PLS(RP2=0.58) and RBFNN (RP2=0.47) calibration models.

Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Choosing appropriate multivariate calibration and preprocessing transformation techniques is important in the determination of soil organic carbon (SOC) content based on visible and near-infrared (Vis–NIR) spectroscopy. The performance levels of partial least-squares regression (PLSR), support vector machine regression (SVMR), and wavelet neural network (WNN) calibration methods coupled with different preprocessing approaches were compared using three kinds of criteria, including the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD). A total of 328 soil samples collected from the south bank of Hangzhou Bay were used as the dataset for the calibration–validation procedure and SOC content inversion. The effects of spectra preprocessing transformation methods were evaluated for raw spectra, Savitzky–Golay smoothing with the first derivatives of reflectance (FDR) and Savitzky–Golay smoothing with logarithm of reciprocal of the reflectance (log R−1). The results indicate that the SVMR is superior to the PLSR, and WNN models for SOC content prediction. The combination of the SVMR model with FDR provided the best prediction results for the SOC content, with R2p = 0.92, RPDP = 2.82, RMSEP = 0.36%, and a kappa correlation coefficient of interpolation as high as 0.97. The FDR of Vis–NIR spectroscopy combined with the SVMR model is recommended over the PLSR and WNN modeling techniques for the high-accuracy determination of the SOC content.

An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

To solve the problem of urban traffic congestion is a major challenge to modern society. Short-term urban traffic flow prediction is the key to realizing traffic control and vehicle guidance on the relief of traffic congestion to make a better city experience. Short-term urban traffic flow is characterized by a high level of time-variation, nonlinearity and randomness. To tackle this problem and to improve the performance of short-term traffic flow prediction, the author of this paper comes up with an improved whale optimization algorithm (IWOA) and tries to introduce it into the wavelet neural network (WNN) so as to optimize its initial weights and wavelet factors via iteration and to establish a short-term traffic flow prediction model based on Wavelet Neural Network with Improved Whale Optimization Algorithm (IWOA-WNN). This model appears to predict traffic flow in a more effective manner and it can remedy the defects of wavelet neural network which usually leads to low prediction accuracy and slow response. The experimental results show that compared with the pure WNN model and WOA-WNN model, the IWOA-WNN model has turned out to be a great improvement in terms of absolute error and percentage of absolute error.

Predicting water sorptivity coefficient in calcareous soils using a wavelet–neural network hybrid modeling approach

Sorptivity (S) coefficient is a measure of liquid absorption/desorption tendency of a porous medium by the capillary. It is a crucial component in hydrological modeling. Since its measurement is usually time-consuming and labor-intensive, the available data sets suffer from the lack of S coefficient. Therefore, its prediction through utilizing efficient numerical approaches (e.g., artificial neural networks, ANNs) has been received increased attention. For this purpose, the wavelet neural network (WNNs) with various wavelet types and decomposition levels was employed. The results were compared to that of two well-known NNs (multilayer perceptron, MLPNNs, and radial-basis function, RBFNNs) and multiple linear regression (MLR). Input attributes consisted of electrical conductivity, pH, initial water content, bulk density, mean weight diameter, geometric mean diameter, organic matter, and calcium carbonate equivalent of a 100-total data set. The performances of the approaches were demonstrated using the field data sets. Correlation-coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency coefficient (NSE) for test data set were 0.87, 0.015, 8.78, and 0.752 for RBFNNs; 0.92, 0.009, 7.19, and 0.798 for MLPNNs; 0.94, 0.005, 6.28, and 0.846 for WNNs, and 0.85, 0.097, 12.70, and 0.515 for MLR, respectively. The WNNs and in particular the ‘sym2′ wavelet function with decomposition level three was able to achieve accurate estimates. The NN models were ranked as RBF > MLP > WNN; WNN > MLP > RBF; MLP > WNN > RBF in terms of easy usability, accuracy and computational time, and cost-effectiveness, respectively. Regarding the predictions obtained, it can be concluded that the applied WNN model is a more efficient tool than the other models to predict S coefficient.

Colour space conversion model from CMYK to CIELab based on CS‐WNN

AbstractIn the process of colour space conversion from CMYK to CIELab, colour difference will be caused, which has a negative impact on the quality of digital printing products. In this article, an improved wavelet neural network (WNN) model optimised by cuckoo search (CS) algorithm is proposed to reduce the colour difference. Initially, the colour space conversion model based on WNN is established. The CS algorithm is used to optimise the initial weights and parameters of dilation and translation in the WNN model. Then, 1296 samples are made to train the CS‐WNN model. Finally, 100 test samples are input into the trained network to obtain the corresponding L, a and b values of CIELab. The experimental results show that the average conversion colour difference () of the proposed model is 3.469. The conversion accuracy and stability of the proposed model are better than the traditional neural network.

Comparative Analysis of Four Neural Network Models on the Estimation of CO2-Brine Interfacial Tension.

During the CO2 injection of geological carbon sequestration and CO2-enhanced oil recovery, the contact of CO2 with underground salt water is inevitable, where the interfacial tension (IFT) between gas and liquid determines whether the projects can proceed smoothly. In this paper, three traditional neural network models, the wavelet neural network (WNN) model, the back propagation (BP) model, and the radical basis function model, were applied to predict the IFT between CO2 and brine with temperature, pressure, monovalent cation molality, divalent cation molality, and molar fraction of methane and nitrogen impurities. A total of 974 sets of experimental data were divided into two data groups, the training group and the testing group. By optimizing the WNN model (I_WNN), a most stable and precise model is established, and it is found that temperature and pressure are the main parameters affecting the IFT. Through the comparison of models, it is found that I_WNN and BP models are more suitable for the IFT evaluation between CO2 and brine.

Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

One of the common non-linear process control problems is the continuous stirred tank reactor problem and the reactor is applied widely in chemical process industries. It is of high importance to develop a suitable controller scheme for the concentration and temperature control of the considered non-linear continuous stirred tank reactor (CSTR) model. In this paper, a novel wavelet neural network (WNN) controller model is developed to carry out the control action and to meet the performance requirements of the system model. The developed wavelet neural network model is tuned for its weights employing the proposed deterministic grey wolf optimizer algorithm and its input is fed from the Mamdani fuzzy model. The optimal number of rules to be formulated for the fuzzy rule base model is computed using the deterministic grey wolf optimizer (DGWO) which is the variant of the classic grey wolf optimizer (GWO). WNN controller devise, based on the inputs from the fuzzy model and weights from DGWO, is designed to perform control action on the non-linear CSTR model. The concentration and temperature control is carried out by the proposed controller and the responses are obtained. Also, the respective time response specifications are evaluated for the developed WNN controller. Simulation experiments prove the effectiveness of the proposed controller for the considered non-linear CSTR model in comparison with that of the existing controllers from the literature.

Meteorological driving factor of eutrophication in Lake Xingyun, Yunnan based on the Coupling Model of Wavelet analysis and Neural Network

Meteorological driving factor of eutrophication in Lake Xingyun, Yunnan based on the Coupling Model of Wavelet analysis and Neural Network

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.

Time Series Modeling of River Flow Using Wavelet Neural Networks

A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables.The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM, IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTECGPS − VTECModel| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.

Dağlık Havzalarda Uydu Kar Verisi ve Dalgacık Sinir Ağı Tabanlı Olasılıklı Akım Modelleme Yaklaşımı

Streamflow prediction is often a challenging issue for snow dominated basins where proper in-situ snow data might be limited and the snow physics is highly complex. The main aim of this study is to propose an alternative modeling solution by considering both accessibility of the inputs and simplicity of the model structure. We propose Wavelet Neural Network (WNN) model approach which takes probabilistic snow cover area in order to produce probabilistic streamflow in the mountainous basins. For the sake of the accessibility of the input data, snow probability maps are produced from cloud-free images of MODIS. The WNN model is trained and tested with observed hydro-meteorological data. Also, MultiLayer Perceptron Model (MLP) is used as a benchmark model. The approach is tested in a snow-dominated headwater (in altitude from 1559 to 3508 m) of Murat River which has a great importance as being one of the main tributaries of Euphrates River. According to the results, the approach is capable of detecting snow distribution in the area of interest and WNN is promising to generate probabilistic streamflow predictions.

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004-2019.

Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China.

Accurate heating, ventilation and air conditioning system load prediction for residential buildings using improved ant colony optimization and wavelet neural network

Accurate prediction of the building load is crucial to ensure the energy saving and improve the operational efficiency of the heating, ventilation, and air conditioning (HVAC) system. In this study, the heating load (HL) and cooling load (CL) of buildings are analyzed using the Spearman method considering eight influencing factors: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. The ant colony optimization (ACO) method is used to optimize the ability of a wavelet neural network (WNN) to predict the HL and CL values of residential buildings. The linearly decreasing inertia weight and self-adaptive mutation operator are introduced to improve the optimizing capability of the ACO. An improved ACO-WNN (I-ACO-WNN) model is proposed to achieve a high-precision building load forecasting, and the formulas including the influence factors of the building load, are proposed. The regression coefficient values of the proposed forecasting model of HL and CL are 0.9714 and 0.9783, respectively. Compared to the traditional WNN model, the root mean square error values of HL and CL predictions by the I-ACO-WNN model are decreased by 66.01% and 73.28%, respectively; while the mean absolute error values are decreased by 82.44% and 84.82%, respectively; also, the mean absolute percentage error values are reduced by 81.21% and 85.31%, respectively; lastly, the mean square error values are reduced by 88.44% and 92.86%, respectively. The proposed prediction model can be used as a reliable tool for HL and CL estimation in future intelligent urban planning.

Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA/SARMA and genetic-algorithm-optimized regression wavelet neural network models

Forecasting energy consumption in buildings is crucial for achieving effective energy management as well as reducing environmental impacts. With the availability of large amounts of relevant data through smart metering, gas consumption forecasting is becoming an integral part of smart building design so that these requirements are met. In this study, we investigate week-ahead forecasting of daily gas consumption in three types of buildings characterized by different gas consumption profiles during a five-year period. As gas consumption in buildings is highly correlated with the average outdoor temperature, regression models with additional residual modeling are used for forecasting. However, conventional regression models with autoregressive moving averages (ARMA) errors (regARMA) perform poorly when the temperature forecasts are inaccurate. To address this, a new forecasting model termed genetic-algorithm-optimized regression wavelet neural network (GA-optimized regWANN) is proposed. It uses the wavelet decomposition of the residuals of temperature regression time-series, which are modeled by multiple nonlinear autoregressive (NAR) models based on sigmoid neural networks. The appropriate delays in the regression vectors of the NAR models are selected using a binary GA. Compared with regARMA and seasonal regARMA, the GA-optimized regWANN model achieved in the three buildings a reduction of 22.6%, 17.7%, and 57% in the mean absolute error (MAE) values in ex post forecasting with recorded temperatures, and a 52.5%, 27%, and 43.6% reduction in the MAE values in ex ante forecasting with week-ahead forecasted temperatures, even under conditions of relatively significant errors in the forecasted temperature.

Short-term Traffic Flow Prediction Based on Harmony Search Algorithm Optimized Wavelet Neural Network

ln order to improve the prediction accuracy of urban road short-term traffic flow, this paper proposes a short-term traffic flow prediction model of wavelet neural networkoptimized by harmony search algorithm(HS-WNN),to solve the problem of slow convergence speed and local optimization when the traditional wavelet neural network one-way gradient descent method is used for parameter optimization. The harmony search algorithm is used to optimize the parameters of the wavelet neural network, and the obtained optimal solution is used to optimize the initial value of the wavelet neural network model, and to predict the short-term traffic flow. And through the simulation experiment of the measured traffic flow data, it is verified that the prediction error of the HS-WNN in this paper is smaller than that of the WNN,HS-WNNhas a higher accuracy.

A new prediction method of seafloor hydrothermal active field based on wavelet neural network

The study of modern seafloor hydrothermal activity and its mineralization has become one of the focuses of global geoscience. Accurate prediction of possible seafloor hydrothermal active fields is the basis of all research work. The detecting method for new seafloor hydrothermal activity still is mainly dependent on marine site investigation. This includes a series of temperature, turbidity, and geochemical anomaly investigations using submarine cameras and manned diving investigation. Both of these require expensive financial, human, and material resources. In order to realize the accurate prediction of potential hydrothermal activity areas in a low cost manner, with strong pertinence, and a wide range, we propose a prediction method of seafloor hydrothermal active region based on Wavelet Neural Network. First, we integrated the hydrothermal position information from the InterRidge Vents Database with the hydrothermal temperature information form the Argo Database to construct a data set. Then, we combined wavelet analysis with an artificial neural network to create a wavelet neural network optimization algorithm. Finally, the temperature and salinity data were input to the wavelet neural network to predict the seafloor hydrothermal active region. Sevenfold cross validation was used to evaluate the performance of the model and 90.43% prediction accuracy was achieved. The results of experiments show that salinity is not related to the existence of hydrothermal activity fields but rather that the surrounding water temperature has a strong correlation with hydrothermal existence. Therefore, it is effectively feasible to use the wavelet neural network model with an input of seawater temperature to predict seafloor hydrothermal activity fields. Although artificial neural network cannot completely replace traditional hydrothermal exploration technology, it can provide a valuable reference with a strong target.

Fault diagnosis of vibration signal of synchronous condenser based on wavelet model

A large synchronous condenser has a complex structure and many parts, which is very prone to failure. However, the regular electrical quantity generally does not change significantly at the initial stage of failure, and may not reflect the fault characteristics. Some non-electrical quantities, such as vibration characteristics, can be used to diagnose and predict faults in a timely manner. Based on this, a multilayer-perceptron based wavelet model is proposed for fault diagnosis. Vibration signals are taken in real time at different positions on the surface of the condenser, and the energy eigenvalues are calculated by fast Fourier transform of the data, and the wavelet neural network model is input for training to obtain the nonlinear mapping relationship between the vibration signals and the fault types of the condenser, so as to realize the fault diagnosis of the condenser. Experimental results show that this method can effectively detect and diagnose the fault of the synchronous condenser in the early stage.