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Optimal wavelet neural network-Based Intrusion Detection in Internet of Things Environment

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Computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： Heba G.Mohamed Fadwa Alrowais Mohammed Abdullah Al-Hagery Mesfer Al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm Algorithm with wavelet neural network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

wavelet neural network Based on NARMA-L2 Model for Prediction of Thermal Characteristics in a Feed System

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Chinese Journal of Mechanical Engineering 2011年第1期24卷 33-41页

作者： JIN Chao WU Bo HU Youmin State Key Laboratory for Digital Manufacturing Equipment and Technology Huazhong University of Science and Teehnology Wuhan 430074 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.

关键词： wavelet neural network NARMA-L2 model particle swarm optimization thermal positioning error feed system

wavelet neural network applied for prognostication of contact pressure between soil and driving wheel

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Information Processing in Agriculture 2014年第1期1卷 51-56页

作者： Hamid Taghavifar Aref Mardani Department of Mechanical Engineering of Agricultural Machinery Faculty of AgricultureUrmia UniversityIran

This paper describes the measurement of contact pressure in the context of wheel–terrain interaction as affected by wheel load and tire inflation pressure when fusion of the wavelet transform with the back-propagation(BP)neural network is applied to construct the wavelet neural network contact pressure prediction model.To this aim,a controlled soil bin testing facility equipped with single-wheel tester was utilized while three levels of velocity,three levels of slippage and three levels of wheel load were applied.Using image processing technique,contact area values were determined which were subsequently used for quantification of contact pressure.Performances of the different predictor models incorporated of various mother wavelets were evaluated using standard statistical evaluation criteria.Root mean square error and coefficient of determination values of 0.1382 and 0.9864 achieved by the optimal wavelet neural network are better than that of BP neural network.The proposed tool typifies a high learning speed,enhanced predicting accuracy,and strong robustness.

关键词： Artificial neural network Contact pressure Mexican hat wavelet wavelet neural network Soil bin

Modeling of coal consumption rate based on wavelet neural network

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第3期8卷 78-92页

作者： Xiaoqiang Wen Shuguang Jian Department of Automation Northeast Electric Power University Jilin P.R.China

In this paper,two wavelet neural network(WNN)frames which depend on Morlet wavelet function and Gaussian wavelet function were established.In order to improve the efficiency of model training,the momentum term was applied to modify the weights and thresholds,and the output of the network was summed up by function transformation of output layer nodes.When the Gaussian wavelet neural networks(GWNN)and Morlet wavelet neural networks(MWNN)were applied to coal consumption rate(CCR)estimation in a thermal power plant,the results confirmed their potency in function approximation.In addition,the influence of learning rate on the models was also discussed through the orthogonal experiment.

关键词： wavelet neural network Morlet wavelet function Gaussian wavelet function estimation coal consumption rate

wavelet neural network Based Traffic Prediction for Next Generation network

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Journal of Southwest Jiaotong University(English Edition) 2005年第2期13卷 113-118页

作者：赵其刚李群湛何正友 School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 China

By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network （NGN） operator. To build a wavelet basis neural network （NN）, the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day＇s traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

关键词： wavelet neural network IP traffic prediction Next generation network wavelet

Identification and Control of Hyperchaotic Lorenz System Based on Quantum Inspired PSO and wavelet neural network

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控制工程期刊（中英文版） 2019年第1期9卷 1-12页

作者： Kun Zhang Wei Zheng School of Mathematics and Statistics Chuxiong normal universityChuxiong Yunnan 675000China

For the identification and control problem of hyperchaotic Lorenz system,a method of feedback compensation control based on quantum inspired PSO and wavelet neural network(PSOQI-wavelet neural network)is proposed.Firstly,the quantum principle obtained from Quantum PSO(QPSO)has been combined with standard PSO to form a new hybrid algorithm called PSO with Quantum Infusion(PSO-QI).Then,the parameters of wavelet neural network were optimized with PSO-QI and feedback compensation control for hyperchaotic Lorenz system is implemented using optimized PSOQI-wavelet neural network.The numerical simulation results showed that this method has better precision and can quickly track given hyperchaotic Lorenz system.

关键词： Hyperchaotic Lorenz System Chaotic Control Quantum Inspired PSO wavelet neural network

RESEARCH ON THE SHORT-TERM AGRICULTURAL ELECTRIC LOAD FORECASTING OF wavelet neural network

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RESEARCH ON THE SHORT-TERM AGRICULTURAL ELECTRIC LOAD FORECA...

APPLICATION OF IMPROVED wavelet neural network TO FAULT DIAGNOSIS OF PUMPING WELLS

第一届国际计算机与计算技术在农业中的应用研讨会

作者： Qian Zhang Department of Economic Management North China Electric Power University

This paper proposes a new method for load forecasting—the wavelet neural network model for daily load forecasting.The neural call function is basis of nonlinear wavelets.A wavelet network is composed by the wavelet basis function.The global optimum solution is got.We overcome the intrinsic defects of a artificial neural network that its learning speed is slow,its network structure is difficult to determine rationally and it produces local minimum points.It can be seen from the example this method can improve effectively the forecast accuracy and speed.It can be applied to the daily agricultural electric load forecasting.

关键词： Artificial neural network wavelet neural network Agricultural electric Load Forecasting

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APPLICATION OF IMPROVED WAVELET NEURAL NETWORK TO FAULT DIAG...

wavelet neural network Disturbance Observer-based Adaptive Robust Tracking Control for Servo System

The 2011 International Conference on Mechanical Engineering and Technology

作者： CHEN Xue-yan

The paper introduced an improved wavelet neural network model based on the certain link between signs and failure of pumping wells. We increased the part of the direct connection between input and output node, and highlighted the correlation of signs input and fault output. The experiment results show that using the improved wavelet neural network for fault diagnosis of pumping wells will greatly enhance the accuracy and speed of pumping wells fault diagnosis.

关键词： wavelet neural network fault diagnosis pumping wells

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Wavelet Neural Network Disturbance Observer-based Adaptive R...