Wavelet Network Model Research Articles

Radio network TETRA path loss calculation by statistical polynomial kernel radial wavelet network models for RSSI predication and comparison in undulating area

Radio network TETRA path loss calculation by statistical polynomial kernel radial wavelet network models for RSSI predication and comparison in undulating area

Improved chimp optimization with deep transfer learning enabled soil classification technique using hyperspectral remote sensing images

Hyperspectral remote sensing (HRS) is commonly employed for crop and soil mapping. Soil and crop classification mapping acts as major roles in planning and management of sustainable development. Thus, it is necessary to design automated crop and soil prediction techniques using HRS images. The recent developments of machine learning (ML) and deep learning (DL) enable to design of effective classification models for soil and crops. This study effectively introduces improved chimp optimization with deep transfer learning enabled crop and soil classification (ICODTL-CSC) technique using HRS images. The presented ICODTL-SC technique aims to identify and categorize different types of crops and soil. To accomplish this, the ICODTL-CSC technique incorporates Adagrad with EffcientNetB0-based feature extractor for producing a collection of feature vectors. In addition, Improved Chimp Optimization Algorithm with a deep adaptive wavelet network model is applied for soil classification, showing the novelty of our work. The performance validation of the presented method is performed using Tamil Nadu Hills dataset, Pavia University, and Salinas scene datasets. The experimental outcomes indicated that the presented method has exhibited the existing techniques in terms of different performance measures.

LSW-Net: A Learning Scattering Wavelet Network for Brain Tumor and Retinal Image Segmentation

Convolutional network models have been widely used in image segmentation. However, there are many types of boundary contour features in medical images which seriously affect the stability and accuracy of image segmentation models, such as the ambiguity of tumors, the variability of lesions, and the weak boundaries of fine blood vessels. In this paper, in order to solve these problems we first introduce the dual-tree complex wavelet scattering transform module, and then innovatively propose a learning scattering wavelet network model. In addition, a new improved active contour loss function is further constructed to deal with complex segmentation. Finally, the equilibrium coefficient of our model is discussed. Experiments on the BraTS2020 dataset show that the LSW-Net model has improved the Dice coefficient, accuracy, and sensitivity of the classic FCN, SegNet, and At-Unet models by at least 3.51%, 2.11%, and 0.46%, respectively. In addition, the LSW-Net model still has an advantage in the average measure of Dice coefficients compared with some advanced segmentation models. Experiments on the DRIVE dataset prove that our model outperforms the other 14 algorithms in both Dice coefficient and specificity measures. In particular, the sensitivity of our model provides a 3.39% improvement when compared with the Unet model, and the model’s effect is obvious.

Satellite-Based Bathymetric Modeling Using a Wavelet Network Model

Accurate bathymetric modeling is required for safe maritime navigation in shallow waters as well as for other marine operations. Traditionally, bathymetric modeling is commonly carried out using linear models, such as the Stumpf method. Linear methods are developed to derive bathymetry using the strong linear correlation between the grey values of satellite imagery visible bands and the water depth where the energy of these visible bands, received at the satellite sensor, is inversely proportional to the depth of water. However, without satisfying homogeneity of the seafloor topography, this linear method fails. The current state-of-the-art is represented by artificial neural network (ANN) models, which were developed using a non-linear, static modeling function. However, more accurate modeling can be achieved using a highly non-linear, dynamic modeling function. This paper investigates a highly non-linear wavelet network model for accurate satellite-based bathymetric modeling with dynamic non-linear wavelet activation function that has been proven to be a valuable modeling method for many applications. Freely available Level-1C satellite imagery from the Sentinel-2A satellite was employed to develop and justify the proposed wavelet network model. The top-of-atmosphere spectral reflectance values for the multispectral bands were employed to establish the wavelet network model. It is shown that the root-mean-squared (RMS) error of the developed wavelet network model was about 1.82 m, and the correlation between the wavelet network model depth estimate and “truth” nautical chart depths was about 95%, on average. To further justify the proposed model, a comparison was made among the developed, highly non-linear wavelet network method, the Stumpf log-ratio method, and the ANN method. It is concluded that the developed, highly non-linear wavelet network model is superior to the Stumpf log-ratio method by about 37% and outperforms the ANN model by about 21%, on average, on the basis of the RMS errors. Also, the accuracy of the bathymetry-derived wavelet network model was evaluated on the basis of the International Hydrographic Organization (IHO)’s standards for all survey orders. It is shown that the accuracy of the bathymetry derived from the wavelet network model does not meet the IHO’s standards for all survey orders; however, the wavelet network model can still be employed as an accurate and powerful tool for survey planning when conducting hydrographic surveys for new, shallow water areas.

Wavelet Network Modeling Method Based on Radiation Characteristics of Array Antenna

In complex electromagnetic environment, the training data contains a lot of noise, which can make classic neural networks overfit easily. In order to solve this problem, a new method based on sampling theory is proposed for constructing models. Through analyzing wavelet networks and radiation fields in the frequency domain, it is proved that the new method can effectively reduce noise on models and help wavelet networks obtain a good generalization. In the simulation, the new method is compared with regularization techniques. The results show that the new method can prevent networks from overfitting and is more robust to noise.

Internal-model control of vehicle chassis based on wavelet-network dynamic inversion method

An internal-model control strategy of vehicle chassis system is proposed based on wavelet-network dynamic inversion method, to eliminate the coupling effect among the chassis subsystems. By analysing the chassis system reversibility, the paired relationship of the input and output variables is determined. Then, a wavelet-network dynamic inversion model of vehicle chassis system is obtained and connected in series with the original system. The chassis system is decoupled into three independent pseudo-linear systems, and decoupling between the control loops of vehicle chassis system is realised. An internal-model controller is designed to improve the system response. Further, a simulation and a driver-in-loop test using LabVIEW PXI and veDYNA are conducted to verify the control strategy efficacy. The results show that the proposed control strategy can eliminate the interference and coupling between the active front-wheel steering, direct yaw-moment control, and active suspension system, and improve the vehicle handling and status tracking stability.

Hybrid harmonic analysis and wavelet network model for sea water level prediction

Accurate sea water level prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction is commonly carried out using only the harmonic analysis (HA-only) model or only a wavelet network (WN-only) model. The harmonic analysis method is the most reliable model for long term sea water level prediction when long data records are available and in contrast the wavelet network method is the most reliable model used for short term sea water level prediction when short data records are available. This paper developed a hybrid harmonic analysis and wavelet network (HA-and-WN) model for accurate sea water level prediction. To validate the hybrid HA-and-WN model, sea water level data from four tide gauges are employed to investigate the performance of the developed hybrid model. It is shown that the majority of error values at 95% confidence level fall within ±14.77cm, ±2.65cm and ±2.08cm range in average with maximum error of 36.84cm, 9.21cm and 7.00cm in average for HA-only model, WN-only model and hybrid HA-and-WN model, respectively. Also, it is found that the root-mean-squared (RMS) errors are about 9.75cm, 1.85cm and 1.49cm for HA-only, WN-only and hybrid HA-and-WN models, respectively, based on the overall performance from the four tide gauges under implementation. Therefore, it is concluded that the developed hybrid HA-and-WN model is superior to the HA-only model by about 85% and outperforms the WN-only model by about 20%, based on the overall RMS errors.

Robust fault detection and isolation scheme using fuzzy wavelet network with a hybrid design algorithm

This paper presents a new robust fault detection and isolation scheme using fuzzy wavelet network based on the bounded error approach. An ecient hybrid design algorithm, which consists of the orthogonal least square and the arti cial bee colony algorithms, is proposed to design fuzzy wavelet network for modeling normal and faulty behaviors of the system. The proposed model provides an alternative description of the behavior of the system with high accuracy, but it su ers from model uncertainty because of model-reality mismatch in practical applications. To overcome this diculty, the bounded error approach inspired from robust identi cation theory is applied to estimate the model uncertainty which de nes a con dence interval of the model output and derives adaptive threshold for residual evaluation. Also, online fault isolation process is performed using fuzzy wavelet network models of the faulty system and analyzing the relation between a bank of residuals. Performance and eciency of the proposed scheme is evaluated by simulating the nonlinear two-tank liquid level control system. Finally, some performance indexes are de ned, and then the Monte-Carlo analysis is carried out to evaluate the reliability and robustness of the proposed scheme.

Nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is shown by simulation. It is illustrated that the proposed method is able to increase the speed of tracking in addition to having very little steady state error. Using chaotic optimization makes the controller robust in the presence of noise.

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

Abstract The paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.

Development of wavelet network model for accurate water levels prediction with meteorological effects

Accurate water levels modeling and prediction is essential for safety of coastal navigation and other maritime applications. Water levels modeling and prediction is traditionally developed using the least-squares-based harmonic analysis method that estimates the harmonic constituents from the measured water levels. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the current state-of-the-art artificial neural network has recently been developed for water levels prediction from short water level measurements. However, a highly nonlinear and efficient wavelet network model is proposed and developed in this paper for water levels modeling and prediction using short water level measurements. Water level measurements (about one month and a week) from six different tide gauges are employed to develop the proposed model and investigate the atmospheric changes effect. It is shown that the majority of error values, the differences between water level measurements and the modeled and predicted values, fall within the −5cm and +5cm range and root-mean-squared (RMS) errors fall within 1–6cm range. A comparison between the developed highly nonlinear wavelet network model and the harmonic analysis method and the artificial neural networks shows that the RMS of the developed wavelet network model when compared with the RMS of the harmonic analysis method is reduced by about 70% and when compared with the RMS of the artificial neural networks is reduced by about 22%. It is also worth noting that if the atmospheric changes effect (meteorological effect) of the air pressure, the air temperature, the relative humidity, wind speed and wind direction are considered, the performance accuracy of the developed wavelet network model is improved by about 20% (based on the estimated RMS values).

Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regression (NNBR), the probability of relative error smaller than 10% increases from 65.79% to 84.21% (forecast periodT=1) and from 51.35% to 91.89%(T=2)by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37%(T=1)and from 81.08% to 91.89%(T=2)by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

In this research, the simulation of Urmia Lake water level fluctuation by means of two models was applied. For this, Support Vector Machines (SVM), and Neural Wavelet Network (NWN) models that conjugated both the wavelet function and ANN, developed for simulating the Urmia Lake water level fluctuation. The yearly data of rainfall, temperature and discharge to the Urmia Lake and water level fluctuation were used. Urmia Lake is the biggest and the hyper saline lake in Iran. The outcome of the SVM based models are compared with the NWN. The results of SVM model performs better than NWN and offered a practical solution to the problem of water level fluctuation predictions. Analysis results showed that the optimal situation occurred with use of precipitation, temperature and discharge for all station and water level fluctuations at the lag time of one year (RMSEs) of 0.23, 0.41 m obtained by SVM, NWN, respectively, and SSEs of 0.43, 1.33 and R 2 of 0.97, 0 obtained by SVM, NWN, respectively. The results of SVM model show better accuracy in comparison with the NWN model.

SAR Image Classification Based on Brushlet and Self-Adaptive Ridgelet Neural Network

Aiming at the defect that BP neural network classification model takes a long time for network training and the condition that wavelet network model lacks of direction information description, the paper presents a method for SAR image classification based on Brushlet and self-adaptive ridgelet neural network. The method extracts the energy and phase feature of SAR image texture through the Brushlet transformation, and inputs the feature vector that describes the energy and phase information into self-adaptive ridgelet neural network for training and classification. The contrast experiment indicates that the classification method proposed in this paper is rapid and accurate, and outperforms the traditional methods.

Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data / Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych

Abstract Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (SVR) and wavelet networks (WN) for daily runoff forecasting in the mountainous Sianji watershed located in the Himalayan region of India. The models were based on runoff, antecedent precipitation index, rainfall, and day of the year data collected over the three year period from July 1, 2001 and June 30, 2004. It was found that both the methods provided accurate results, with the best WN model slightly outperforming the best SVR model in accuracy. Both the WN and SVR methods should be tested in other mountainous watershed with limited data to further assess their suitability in forecasting.

A Novel Two-Stage Classical Gram-Schmidt Algorithm for Wavelet Network Construction

This paper proposes a two-stage orthogonal least squares (OLS) algorithm based on the classical Gram-Schimdt (CGS) method for the construction of wavelet networks. The main objective is to improve the compactness of the wavelet networks model built by the orthogonal forward stepwise methods. The proposed two stage stepwise method selects model terms one by one from a candidate term pool to construct an initial model in the first stage, and then replaces some insignificant terms by reviewing their contributions to the cost function in the second stage, leading to a significantly improved compact model. The efficacy and effectiveness of the proposed technique is illustrated by a numerical example.

Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition

in this paper, we propose a novel architecture of wavelet network called Multi-input Multi-output Wavelet Network MIMOWN as a generalization of the old architecture of wavelet network. This newel prototype was applied to speech recognition application especially to model acoustic unit of speech. The originality of our work is the proposal of MIMOWN to model acoustic unit of speech. This approach was proposed to overcome limitation of old wavelet network model. The use of the multi-input multi-output architecture will allows training wavelet network on various examples of acoustic units.

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO2, NO, O3, SO2 and PM2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO2, NO, O3, SO2 and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed‐forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%–50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.