Optimized Wavelet Neural Network Research Articles

Hybrid approach for 3 phase bridgeless AC to DC Cuk converter for WECS

ABSTRACT This manuscript proposes a novel hybrid method based on a single-stage three-phase bridgeless alternating current (AC) to direct current (DC) Cuk converter for wind energy conversion systems (WECS). The proposed hybrid method is the combination of Mexican Axolotl Optimization (MAO) and Fuzzy Wavelet Neural Network (FWNN), hence it is named as MAO-FWNN method. Moreover, the proposed method enhanced the system performance by distributing the defined reactive-power to harmonic reduction and doubly fed induction generator (DFIG). The proposed converter is modelled under a discontinuous mode of the inductor current. The converter’s output inductor is modelled as a continuous inductor current mode, and it functions to provide enhanced power quality (PQ) and low DC (Direct Current) voltage ripple. At that point, the MATLAB platform is used to execute the performance of the proposed method, and it is compared with existing methods. The performance of the proposed method provides the 0.095 to 0.096 times/sec, which is higher than the existing methods. As a result, the proposed technique is well established compared to the existing techniques.

A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port

Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed.

Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

The high accurate detection of blood glucose level (BGL) is very important for non-invasive monitoring of diabetes mellitus. In this work, the optoacoustic (OA) quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts of multiple factors (irradiation energy, concentration, temperature, flow rate and vessel depth) was firstly studied. To achieve this aim, a set of OA in vitro detection system of blood glucose with the comprehensive influence of five factors was constructed. The real-time OA signals of 625 rabbit whole blood were obtained at the characteristic wavelength of 750 nm, as well as peak-to-peak values (PPVs). Results show that the accurate detection of BGL was very difficult due to the complicated OA signals. To accurately predict the BGL under the comprehensive impacts of five factors, wavelet neural network (WNN) was employed to train BGL of 500 training set blood. The mean square error (MSE) of BGL for 125 testing set blood was 6.5782 mmol/L. To decrease the MSE, WNN optimized by quantum particle swarm optimization (QPSO), i.e., QPSO-WNN algorithm was utilized. The MSE of BGL based on QPSO-WNN was 0.37485 mmol/L, which was superior to 0.48005 mmol/L of PSO-WNN. Particularly, to further decrease MSE, a novel nonlinear dynamic shrinkage coefficient (DSC) strategy was proposed, and compared with other four kinds of DSC strategies and the fixed one. With the optimal parameters, the MSE of BGL was decreased to 0.3088 mmol/L. Comparison results of seven algorithms and research works demonstrate that OA technology combined with QPSO-WNN algorithm and the novel nonlinear DSC strategy has excellent performance in the quantitative detection of diabetes mellitus involving in the comprehensive impacts.

Special issue on International Conference on Computing and Communication Networks

Special issue on International Conference on Computing and Communication Networks

Intelligent metaheuristics with optimal machine learning approach for malware detection on IoT‐enabled maritime transportation systems

AbstractThe latest advancements in Internet of Things (IoT) have revolutionized the productivity of global shipping industry in the recent years. It also led to the emergence of IoT‐enabled Maritime Transportation Systems (MTS). These approaches detect the malware in network before the execution process. Various machine learning (ML) models have been proposed and designed in literature for effective malware detection. However, the existence of numerous features in the data bring dimensionality problem which can be only resolved by the use of feature selection approaches. Therefore, the current research work presents Intelligent Metaheuristics‐based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML‐MD) on IoT‐enabled MTS. Primarily, IMFSOML‐MD technique involves the design of Quantum Invasive Weed Optimization Algorithm‐based Feature Selection technique to optimally choose a subset of features. Moreover, an Optimal Wavelet Neural Network (OWNN) model is employed to perform classification process. The initial parameters of WNN model are optimally tuned with the help of Colliding Bodies Optimization algorithm thereby improving the detection performance. The proposed IMFSOML‐MD technique was experimentally validated using publicly‐available CICMalDroid2020 dataset. The results from extensive comparative analysis demonstrated the superiority of the proposed IMFSOML‐MD technique over other compared methods in terms of detection performance with maximum accuracy of 98.96%.

PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods.

Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process's 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.

Photoacoustic identification of blood authenticity based on quantum-behaved particle swarm optimized wavelet neural network.

To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time-resolved photoacoustic signals and peak-to-peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak-to-peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum-behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN-PSO algorithms. Results showed the correct rate of WNN-QPSO was increased to 96%. Then, three kinds of dynamic contraction-expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN-QPSO coupled with TMSS and dynamic contraction-expansion coefficient had an excellent performance in the identification of blood authenticity.

Research on Prediction Method of Distribution Network Activity in Green Electric Power through PSO-WNN

The convergence and prediction accuracy of the artificial neural network prediction model are affected by the initial parameter settings of the model, and the wavelet neural network (WNN) modeling algorithm can effectively overcome this defect and achieve better prediction results. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the initial weights, and the optimal value of the particle swarm algorithm is assigned to the WNN to replace the initial random assignment of the WNN. The WNN is trained according to the backpropagation (BP) algorithm until convergence. The particle swarm optimization wavelet neural network (PSO-WNN) model is used to predict the cost of a single grid activity based on the cost driver parameters of the power grid. The prediction error analysis shows that the model significantly improves the accuracy of the activity cost prediction, which is beneficial to the grid enterprise to effectively manage the cost drivers and track the specific effects of the grid activity cost which produced by the driver parameters.

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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.

LED Landscape Lighting Equipment Fault Diagnosis Research

Aiming at the fault diagnosis characteristics of LED landscape lighting equipment, a class of genetic algorithm improved particle swarm optimization optimized wavelet neural network model is constructed. This fusion algorithm introduces the idea of cross factors and inertia weights in the genetic algorithm to the basic particle swarm optimization algorithm, and adjusts for the traits of the standard wavelet neural network that has a slow convergence rate and might fall into local extreme values. The simulation results prove that this fusion algorithm can be efficaciously applied to the fault diagnosis of LED landscape lighting equipment and meet the needs of real-time monitoring of equipment.

A high-precision short-term prediction method with stable performance for satellite clock bias

Real-time users cannot carry out real-time precise point positioning (RT-PPP) because they cannot receive real-time service (RTS) products from the international GPS service (IGS) in the case of interrupted communication. We address this issue by introducing a stable particle swarm optimized wavelet neural network (PSOWNN) to predict the short-term satellite clock bias in real time accurately. The predicted sequences of the new model are compared with those of the conventional linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Kalman filter models. The results show that the accuracy of the proposed model is better than that of these four models. The average prediction accuracy of the 30-min and 60-min forecasting has improved by approximately (79.3, 82.4, 79.1, 97.4) % and (97.4, 82.9, 87.7, 98.9) % and is better than 0.3 ns during 30-min and 1-h forecasting. The RTS products can thus be replaced with the short-term clock products predicted by the PSOWNN model to meet the precision requirements of RT-PPP.

Prediction of subsidence deformation of subway project by improved markov chain and optimized wavelet neural network model

In view of the fact that the factors affecting the subsidence deformation of subway engineering are complex and the subsidence monitoring data are random, this paper constructs an optimized wavelet neural network model with markov chain improvement to improve the accuracy of the predicted data. Firstly, the wavelet neural network is optimized by adding momentum and adjusting learning rate adaptively. Finally, based on markov chain theory, the residual between the predicted value and the actual monitoring value of the optimized wavelet neural network is improved. Taking the settlement data of a settlement monitoring point of xi'an metro line 14 as the research object, the prediction accuracy of the model was compared and analyzed by C++ program. The results of the program show that the additional momentum and the adaptive learning rate are effective for the optimization of the wavelet neural network model, and the prediction accuracy of the optimized wavelet neural network model improved by the markov chain is higher than that of the single model, which is suitable for the prediction of the subway engineering settlement.

Hybrid FruitFly Optimization Algorithm and Wavelet Neural Network for Energy Efficiency in WSN

Hybrid FruitFly Optimization Algorithm and Wavelet Neural Network for Energy Efficiency in WSN

Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN

Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.

A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

Nowadays, using cloud computing services is becoming very popular among corporations and dependent users because of its flexibility and diverse facilities. So, demand for its infrastructures is increasing exponentially and with this huge growth of demands, it is getting more challenging for cloud providers to serve all the user requests in a way that quality of service is high enough and service level agreement is also met. Along years, researchers found out that for an optimized resource allocation which provides required user quality of service it is needed to know the amount of future workload in advance so resources can be prepared. There are many methods introduced for cloud workload prediction but excessive changes in the number of requests in cloud workloads cause a reduction in their prediction accuracy. To solve this problem, we have proposed a novel hybrid wavelet neural network method which can efficiently model excessive changes in workload and predict the upcoming requests. For training the proposed method accurately, we have used two heuristic algorithms, Artificial Immune System and Water Cycle Algorithm. The two mentioned heuristic algorithms are used for finding optimized parameters (such as bias and weight) for the wavelet neural network algorithm. The proposed wavelet neural network trained with heuristic algorithms is tested on real and standard cloud workloads. At last, the accuracy of the proposed method is thoroughly surveyed statistically and also compared with other state-of-the-art prediction methods. Simulation results show that our method improves MAPE error at least 10% in comparison to other rival prediction methods.

Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network

ABSTRACTIn order to solve the disadvantages of the traditional wavelet neural network (WNN) algorithm applied in transformer fault diagnosis, such as uneven sample distribution of training samples and high diagnostic error rate and long training time, an improved fault diagnosis method is proposed based on fuzzy clustering and the flower pollination algorithm. Firstly, fuzzy clustering is applied to deal with transformer fault sample data so as to remove the bad data; secondly, the flower pollination algorithm is applied to obtain the optimal parameters of the WNN. The example analysis results show that WNN based on the flower pollination algorithm (FPA-WNN) has better convergence, lower diagnosis error rate and shorter training time compared with WNN based on the particle swarm algorithm (PWA-WNN) and it is more suitable for transformer fault diagnosis.

A novel modeling approach and its application in polymer quality index prediction

Accurate and reliable quality index prediction is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a novel modeling approach for quality index prediction based on optimal fuzzy wavelet neural network (FWNN) with improved gravitational search algorithm (IGSA), where the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model is replaced with wavelet neural network (WNN). Then, an online learning algorithm of the FWNN model is derived by using gradient descent algorithm, and an IGSA algorithm is proposed to online adapt the learning rates of FWNN. Research on the proposed soft sensor is carried out with the data from a real industrial PP plant, and the results are compared among the WNN, FWNN and IGSA-FWNN models. The research results show that the proposed prediction model achieves a good performance in practical industrial quality index, melt index, prediction process.

Function-Aware Anomaly Detection Based on Wavelet Neural Network for Industrial Control Communication

Function control, which is an essential link in industrial automation, is undergoing a growing integration with ICTs (Information Communication Technologies) because of the flexible manufacturing and convenient interoperability in CPSs (Cyber-Physical Systems). However, it has also brought the increasing dangers of cyberattacks caused by malicious or intentional industrial process control exploitations. In order to effectively detect these cyber intrusions and anomalies, this paper proposes a function-aware anomaly detection approach based on WNN (Wavelet Neural Network), which perceives the abnormal function control changes in industrial control communication. By appropriately extracting the time-related function control characteristics from industrial communication packets, this approach builds an optimized wavelet neural network to model the normal function control behaviors and calculates the detection threshold to differentiate the aberrant industrial process control activities. Additionally, a real-world control system, whose communication protocol is Modbus/TCP, is simulated to furnish the analyzed function control data. According to the experimental results, we fully demonstrate this approach has the fine detection accuracy and adequate real-time capability.

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.