Wavelet Neural Network Approach Research Articles

Machine learning for enhancing manufacturing quality control in ultrasonic nondestructive testing: A Wavelet Neural Network and Genetic Algorithm approach

With the rapid development of the global manufacturing industry, an efficient and accurate quality control system has become key to enhancing competitiveness. Ultrasonic Nondestructive Testing (NDT), as an efficient means of quality inspection, plays a crucial role in improving manufacturing quality through the precision of its data analysis. This study aims to explore the application of ultrasonic NDT data in manufacturing quality control by integrating machine learning technologies, with a specific focus on the Wavelet Neural Network optimized by Genetic Algorithms (GA-WNN). This study achieved significant prediction and evaluation results by applying a GA-WNN to quality control in manufacturing. Compared to traditional Wavelet Neural Network (WNN) models, the GA-WNN more effectively identifies and predicts potential quality issues, especially in noisy data and complex production environments, demonstrating higher accuracy and stability. When predicting possible defect types in the manufacturing process, the GA-WNN showed a notable improvement in accuracy over other models. Additionally, in quality stability evaluation, GA-WNN was able to capture production fluctuations more accurately, providing more valuable results for decision-making. The methodologies and discoveries of this study offer new perspectives and tools for quality control in manufacturing and the analysis of ultrasonic NDT data, presenting broad application prospects.

Parkinson's disease classification with CWNN: Using wavelet transformations and IMU data fusion for improved accuracy.

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments and various other symptoms. Early and accurate classification of PD patients is crucial for timely intervention and personalized treatment. Inertial measurement units (IMUs) have emerged as a promising tool for gathering movement data and aiding in PD classification. This paper proposes a Convolutional Wavelet Neural Network (CWNN) approach for PD classification using IMU data. CWNNs have emerged as effective models for sensor data classification. The objective is to determine the optimal combination of wavelet transform and IMU data type that yields the highest classification accuracy for PD. The proposed CWNN architecture integrates convolutional neural networks and wavelet neural networks to capture spatial and temporal dependencies in IMU data. Different wavelet functions, such as Morlet, Mexican Hat, and Gaussian, are employed in the continuous wavelet transform (CWT) step. The CWNN is trained and evaluated using various combinations of accelerometer data, gyroscope data, and fusion data. Extensive experiments are conducted using a comprehensive dataset of IMU data collected from individuals with and without PD. The performance of the proposed CWNN is evaluated in terms of classification accuracy, precision, recall, and F1-score. The results demonstrate the impact of different wavelet functions and IMU data types on PD classification performance, revealing that the combination of Morlet wavelet function and IMU data fusion achieves the highest accuracy. The findings highlight the significance of combining CWT with IMU data fusion for PD classification using CWNNs. The integration of CWT-based feature extraction and the fusion of IMU data from multiple sensors enhance the representation of PD-related patterns, leading to improved classification accuracy. This research provides valuable insights into the potential of CWT and IMU data fusion for advancing PD classification models, enabling more accurate and reliable diagnosis.

Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.

A novel multi-branch wavelet neural network for sparse representation based object classification

Recent advances in acquisition and display technologies have led to an enormous amount of visual data, which requires appropriate storage and management tools. One of the fundamental needs is the design of efficient image classification and recognition solutions. In this paper, we propose a wavelet neural network approach for sparse representation-based object classification. The proposed approach aims to exploit the advantages of sparse coding, multi-scale wavelet representation as well as neural networks. More precisely, a wavelet transform is firstly applied to the image datasets. The generated approximation and detail wavelet subbands are then fed into a multi-branch neural network architecture. This architecture produces multiple sparse codes that are efficiently combined during the classification stage. Extensive experiments, carried out on various types of standard object datasets, have shown the efficiency of the proposed method compared to the existing sparse coding and deep learning-based methods.

Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems

Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.

A hybrid ARIMA–WNN approach to model vehicle operating behavior and detect unhealthy states

As modern vehicles system becomes increasingly complex, there is an urgent need to develop a framework to monitor the behavior and detect the unhealthy states to appropriately arrange the maintenance in order to extend the vehicle life cycle. Sensors installed in vehicles are able to record a huge amount of multiple channel time series data. This paper develops a prediction model and an unhealthy state detection strategy for monitoring the behavior of the operating vehicle by analyzing multiple channel time series data channels. Due to the complexity of the underlying process of multiple channel time series data, a hybrid ARIMA–WNN model is proposed by combining the Auto-Regressive Integrated Moving Average (ARIMA) and Wavelet Neural Network (WNN) models. This model exploits the ARIMA capacity for predicting the unseen patterns without labeled historical data and WNN’s flexibility in analyzing different variations of time series data. Furthermore, a threshold-based anomaly detection strategy is developed to timely recognize the unhealthy states of the vehicle. To test the performance of the proposed hybrid ARIMA–WNN, a large scale multiple-channel time series data including three months of second-wise records of 101 channels of an operating vehicle is used. Results reveal the higher accuracy of the proposed ARIMA–WNN compared with the ARIMA and WNN models in modeling the behavior of different data channels. By estimating the proper distribution of the threshold, the proposed anomaly detection method is also efficient for identifying the unhealthy states of the vehicle.

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.

Forecasting Analysis of Covid-19 Cases with Wavelet Neural Network and Time Series Approach

COVID-19 is a respiratory disease caused by a novel coronavirus first detected in December 2019. As the number of new cases increases rapidly, pandemic fatigue and public disinterest in different response strategies are creating new challenges for government officials in tackling the pandemic. Therefore, government officials need to fully understand the future dynamics of COVID-19 to develop strategic preparedness and flexible response planning. In the light of the above-mentioned conditions, in this study, autoregressive integrated moving average (ARIMA) time series model and Wavelet Neural Networks (WNN) methods are used to predict the number of new cases and new deaths to draw possible future epidemic scenarios. These two methods were applied to publicly available data of the COVID-19 pandemic for Turkey, Italy, and the United Kingdom. In our analysis, excluding Turkey data, the WNN algorithm outperformed the ARIMA model in terms of forecasting consistency. Our work highlighted the promising validation of using wavelet neural networks when making predictions with very few features and a smaller amount of historical data.

Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach

SummaryIn this article, we develop a novel method to design a controller for nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy model in the presence of unknown dynamics, uncertainties in parameters of nonlinear system and external disturbances. The control law is constituted two segments. The first segment derives from parallel distributed compensation (PDC) procedure, in which each control rule is drawn from the respective rule of T‐S fuzzy model. The second segment stems from fuzzy wavelet neural network (FWNN) estimator which is evoked by the hypothesis of multiresolution analysis (MRA) of wavelet transforms and fuzzy notions, so as to approximate the uncertainties and external disturbances in T‐S fuzzy model accurately. In this regard, the Lyapunov stability theorem is applied to acquire the adaptive learning laws for training and tuning online FWNN parameters such as the dilations, translations, and the weights of networks. Moreover, the asymptotic stability of the closed loop system is guaranteed based on the Lyapunov stability theorem. Furthermore, a development of the proposed method to observer‐based controller design for uncertain nonlinear systems descripted by T‐S fuzzy model is provided. The efficiency and robustness of the proposed method are illustrated by simulation outcomes. It is noteworthy that the proposed controller remarkably handles the uncertainties and external disturbances in T‐S fuzzy model without employing traditional conservative lemma and without considering bounds on uncertainties.

An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting

Due to high penetration of distributed energy resources, integration of intermittent renewable energy resources and deployment of demand-side management, highly accurate short-term load forecasting becomes increasingly important. This paper proposes a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The full wavelet packet transform model is used to decompose the load profile and various features into several components with different frequencies and these components are used to train the neural networks. To perform load forecasting, the full wavelet packet transform model decomposes features into various components that are fed into the trained neural networks, and the outputs of the neural networks are constructed as the forecasted load. The proposed model is applied for load prediction in the electric market of Ontario, Canada. Simulation results show that the proposed approach reduces the mean absolute percentage error (MAPE) by 20% in comparison with the traditional neural network method. The proposed approach can be used by utilities and system operators to forecast electricity consumption with high accuracy, which is highly demanded for renewable energy integration, demand-side management and power system operation.

A wavelet neural network approach to predict daily river discharge using meteorological data

This paper reports some part of modelling and data analysis work carried out within the frame of a comprehensive project on the web-based development of watershed information system. This work basically aims to present the daily discharge predictions from the actual discharge along with the meteorological data using a wavelet neural network approach, which combines two methods: discrete wavelet transform and artificial neural networks. The wavelet–artificial neural network model developed provides a good fit with the measured data, in particular with zero discharge in the summer months and also with the peaks and sudden changes in discharge on the test data collected throughout the year. The results indicate that the wavelet–artificial neural network model based predictions are distinctly superior to that of conventional artificial neural network model that corresponds up to an 80% reduction in the mean-squared error between the artificial neural network model and measured data.

Hybrid Wavelet Neural Network Approach for Daily Inflow Forecasting Using Tropical Rainfall Measuring Mission Data

AbstractA novel wavelet-artificial neural network hybrid model (WA-ANN) for short-term daily inflow forecasting is proposed, using for the first time Tropical Rainfall Measuring Mission (TRMM) data...

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.

Performance comparison of novel WNN approach with RBFNN in navigation of autonomous mobile robotic agent

This paper addresses the performance comparison of Radial Basis Function Neural Network (RBFNN) with novel Wavelet Neural Network (WNN) of designing intelligent controllers for path planning of mobile robot in an unknown environment. In the proposed WNN, different types of activation functions such as Mexican Hat, Gaussian and Morlet wavelet functions are used in the hidden nodes. The neural networks are trained by an intelligent supervised learning technique so that the robot makes a collision-free path in the unknown environment during navigation from different starting points to targets/goals. The efficiency of two algorithms is compared using some MATLAB simulations and experimental setup with Arduino Mega 2560 microcontroller in terms of path length and time taken to reach the target as an indicator for the accuracy of the network models.

Wavenet using artificial bee colony applied to modeling of truck engine powertrain components

The purpose of this paper is to validate an artificial wavelet neural network, or wavenet model, combined with artificial bee colony optimization, a swarm intelligence paradigm, to model powertrain components of a truck engine. The arrangement of artificial neural networks with wavelet based functions, called artificial wavelet neural network or wavenet (AWNN), creates a valuable tool to represent the nonlinear multivariable systems. AWNN can be considered a particular case of the feed-forward basis function neural network model. To illustrate the use of the proposed AWNN based on ABC optimization for the black-box modeling, we apply it to model a truck engine with a cubic displacement greater than seven liters. Identification results were carried out using AWNN implemented in Matlab computational environment and the model accuracy is evaluated based on performance indices. Final results were compared with Elman network, Jordan network and kernel adaptive filtering in order to check the AWNN performance. The comparison methods were tuned with the same optimization algorithm in order to find their best tuning parameters. The simulation results show that the artificial wavelet neural network approach can be useful and a promising technique in powertrain components modeling. This proposed AWNN combined with artificial bee colony approach allows modeling the dynamical behavior of powertrain components of a truck engine.

Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE<10%, ρ>0.94) compared to FFANN (NRMSE<16%, ρ>0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.

Road profile estimation using wavelet neural network and 7-DOF vehicle dynamic systems

Road roughness is a broad term that incorporates everything from potholes and cracks to the random deviations that exist in a profile. To build a roughness index, road irregularities need to be measured first. Existing methods of gauging the roughness are based either on visual inspections or using one of a limited number of instrumented vehicles that can take physical measurements of the road irregularities. This paper more specifically focuses on the estimation of a road profile (i.e., along the “wheel track”). This paper proposes a solution to the road profile estimation using a wavelet neural network (WNN) approach. The method incorporates a WNN which is trained using the data obtained from a 7-DOF vehicle dynamic model in the MATLAB Simulink software to approximate road profiles via the accelerations picked up from the vehicle. In this paper, a novel WNN, multi-input and multi-output feed forward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feed forward network. The training formulas based on BP algorithm are mathematically derived and a training algorithm is presented. The study investigates the estimation capability of wavelet neural networks through comparison between some estimated and real road profiles in the form of actual road roughness.

Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time

Recently Artificial Neural network (ANN) was extensively used as non-linear inter-extrapolator for ocean wave forecasting as well as other application in ocean engineering. In this current study, the Wavelet transform was hybridised with ANN naming Wavelet Neural Network (WLNN) for significant wave height forecasting near Mangalore, west coast of India, upto 48h lead time. The main time series of significant wave height data were decomposed to multiresolution time series using discrete wavelet transformations. Then, the multiresolution time series data were used as input of the ANN to forecast the significant wave height at different multistep lead time. It was shown how the proposed model, WLNN, that makes use of multiresolution time series as input, allows for more accurate and consistent predictions with respect to classical ANN models. The proposed wavelet model (WLNN) results revealed that it was better forecasted and consistent than single ANN model because of using multiresolution time series data as inputs.

Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting

In view of the defects of the prediction model based on neural network, such as when doing prediction of nonlinear sequence, it is likely to fall into local hypo-strong point, and the rate of training is very slow. This paper presents a fuzzy wavelet neural network (FWNN) approach for annual electricity consumption in high energy consumption city. It is claimed that, due to high fluctuations of energy consumption in high energy consumption cities, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual electricity consumption. Furthermore, the FWNN approach based on ANN is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) from 1983 to 2003 is used to illustrate the applicability of the FWNN approach. This is the first study to present an algorithm based on the ANN and wavelet for forecasting long term electricity consumption in high energy consuming city. The prediction effect of wavelet neural network prediction model is proved in matlab7.0 simulation environment. A better prediction result is gained, and the defect of falling into local hypo-strongpoint is overcome, at the same time, the rate of training is raised compared with the prediction model based on artificial neural network. The calculation result shows that the presented model is effective.