Improved wavelet neural network based on change rate to predict satellite clock bias

In 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.

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

A data-and-knowledge-driven WNN modeling approach for the absorption refrigeration system

Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting the average post-dry out Nusselt number for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem.

Wheel and rail wear seriously affects the safety and reliability of train operations. In this study single-carriage and double-carriage models considering the connecting unit of a high-speed train are developed to investigate the normal forces, lateral forces, and lateral displacements of wheelsets. Based on the results from these models, the Archard wear model is employed to predict the wheel wear. In addition, based on the daily measured data, a nonlinear autoregulatory (NAR) model and a wavelet neural network (WNN) model are developed to predict the wheel wear over a longer time period. The simulation results show that, compared with the single-carriage model, the normal forces, lateral forces, and lateral displacements of the wheelsets close to the connecting unit in the double-carriage model increase to a certain extent dependent on the speed. The wheel wear predictions show that the wheel wear on the wheelsets near the connecting unit is slightly larger than on the wheelsets far from the connecting unit. Based on the mean square error, the NAR model has somewhat better performance in the wheel wear prediction than the WNN model. The research results represent an important contribution to the maintenance and safe operation of high-speed trains.

In this study, a wavelet neural network (WNN) model for predicting critical heat flux (CHF) is set up. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of its globe optimal searching, quick convergence speed and solving non-linear problem. The database used in the analysis is from the 1960’s, including 126 data points which cover these parameter ranges: pressure P=100–1,000 kPa, mass flow rate G=40–500 kgm-2s-1, inlet subcooling ΔTsub=0–35◦C and heat flux Q=20–8,000 kWm-2. The WNN prediction results have a good agreement with experimental data. Simulation and analysis results show that the network model can effectively predict CHF.

A model of wavelet neural network (WNN) using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of Particle Swarm Optimization (PSO) and gradient descent algorithm (GD), and is thus called HGDPSO. The Particle Swarm Optimizer has previously been used to train neural networks and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. HGDPSO was proposed for neural network training to avoid premature and eliminate stagnation in PSO. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. This study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures. Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. The study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis. The obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

The groundwater is a significant source of readily available freshwater. However, these groundwater aquifers are susceptible to contamination by the presence of many potential pollution sources. These potential sources may be active at any location for any duration with an unknown magnitude of source fluxes. The identification of these potential sources is necessary for remediation and management. In this work, the groundwater contamination sources are identified for two probable situations, i.e., Error-free concentration measurements and erroneous concentration measurements. The proposed methodology exploits the capability of the multistage approach of recent hybrid soft computing models (ensemble of Wavelet Neural Network [WNN]; Autoregressive Integrated Moving Average [ARIMA]) to identify potential pollution sources effectively. The models are trained by the simulated concentrations for randomly generated source fluxes by Groundwater Modelling System (GMS). The multistage approach has been adopted here, which includes three-stage models. To exploit the effectiveness of WNN and ARIMA, they are used at different stages in the multistage models, thereby developing various hybrid ensembles. Total of four hybrid models are developed utilising the multi-staging approach of WNN and ARIMA models, Namely Hybrid-II, III, IVA, and IVB. Predicted results from these models were compared on the same data sets. They are compared using various performance indices, such as RMSE, NE, correlation coefficient, etc. Error values of Hybrid II ensemble (employing WNN at first stage and ARIMA at second stage) are lowest as compared to only WNN and other Hybrid models. Hence, the Hybrid-II model can be utilised to accurately predict potential contamination sources in any sample area in groundwater.

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

In a satellite navigation system, high-precision prediction of satellite clock bias directly determines the accuracy of navigation, positioning, and time synchronization and is the key to realizing autonomous navigation. To further improve satellite clock bias prediction accuracy, we establish a satellite clock bias prediction model by using long short-term memory (LSTM) that can accurately express the nonlinear characteristics of the navigation satellite clock bias. Outliers in the original clock bias should be preprocessed before using the clock bias for prediction. By analyzing the working principle of the traditional median absolute deviations method, the ambiguity of the mathematical model of that method was improved. Experimental results show that the improved method is better than the traditional method at detecting gross errors. The single difference sequence of the preprocessed satellite clock bias was taken as the research object. First, a quadratic polynomial model was fit to the trend term of the single difference sequence. Second, based on the LSTM neural network model and the basic principles of supervised learning, a supervised learning LSTM network model (SL-LSTM) was proposed that models cyclic and random terms. Finally, the prediction function of the satellite clock bias was realized by extrapolating the model by adding a trend term. We adopt the GPS precision satellite clock bias of International GNSS Service data forecast experiments and apply wavelet neural network (WNN), autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models to compare their prediction effects. The average prediction RMSE for 3 h, 6 h, 12 h, 1 d, and 3 d based on the SL-LSTM improved by approximately −21.80, −1.85, 8.57, 2.27, and 40.79%, respectively, compared with the results of the WNN. The average prediction RMSE based on the SL-LSTM improved by approximately 38.23, 65.48, 80.22, 85.18, and 94.51% compared with the ARIMA results. The average prediction RMSE based on the SL-LSTM improved by approximately 82.37, 75.88, 67.24, 45.71, and 58.22% compared with the QP results. Compared with the WNN, the SL-LSTM method has no obvious advantages in the prediction accuracy and stability in short-term prediction but achieves a better long-term prediction accuracy and stability. With an increased prediction duration, the SL-LSTM method is clearly better than the other three methods in terms of the prediction accuracy and stability. The results indicated that the quality of satellite clock bias prediction by the SL-LSTM method is better than that of the above three methods and is more suitable for the middle- and long-term prediction of satellite clock bias.

A non‐parametric system identification‐based model is presented for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network (WNN) model developed by the authors. The model does not require complete measurements of the dynamic responses of the whole structure. A large structure is divided into a series of sub‐structures around a few pre‐selected floors where sensors are placed and measurements are made. The new model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively, thus resulting in fast training convergence and high accuracy. A new damage evaluation method is proposed based on a power density spectrum method, called pseudospectrum. The multiple signal classification (MUSIC) method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using the data obtained for a 38‐storey concrete test model. The results demonstrate the effectiveness of the WNN model together with the pseudospectrum method for damage detection of highrise buildings based on a small amount of sensed data. Copyright © 2007 John Wiley & Sons, Ltd.

Due to the time-varying and uncertainty of traffic flow, the Intelligent Traffic System (ITS) is applied more and more widely in many cities. Accurate short-term traffic flow forecasting is the core component of the system. An improved Wavelet Neural Network (WNN) prediction model based on the Genetic Algorithm (GA) is put forward in this paper. The Genetic Algorithm proposed according to the law of biological evolution is introduced, and the crossover and mutation probability of the algorithm is improved in this paper. The stretching scale and translation scale of the wavelet function are trained firstly, and then the parameters obtained after the pre-trained are further optimized by adopting the gradient descent method. It overcomes the limitation that the traditional WNN is apt to get the local minimum when optimizing parameters. The simulation results indicate that the WNN model based on the improved Genetic Algorithm (IGA) can forecast the short-term traffic flow more precisely.

The fluorescence spectrum of pesticides whose structures are very similar overlap in a certain wavelength range. To the classification and recognition of overlapping fluorescence spectrum, BP network has the shortcomings of slow training speed and high error rate. An improved wavelet neural network (WNN) is presented in this paper. The network topology is given, wavelet basis is selected and its network algorithm is designed to carry out the design of experimental system. By using the WNN and BP network separately, the simulation research of fluorescence spectrum classification of carbofuran and carbaryl has been done. The results show that WNN has a higher resolution. To minor structural differences of spectrum, it has a better recognition capability and higher measuring accuracy.

In this paper we have used a local linear wavelet neural network (LLWNN) model for pattern classification. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. Particle swarm optimisation (PSO) technique used for training the LLWNN. Simulation results for the classification of different benchmark datasets show the feasibility and effectiveness of the proposed method.