Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines

With the continuous increase in the proportional use of wind energy across the globe,the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consideration for research.Therefore,it is crucial to accurately analyze the thickness of icing on wind turbine blades,which can serve as a basis for formulating corresponding control measures and ensure a safe and stable operation of wind turbines in winter times and/or in high altitude areas.This paper fully utilized the advantages of the support vector machine(SVM)and back-propagation neural network(BPNN),with the incorporation of particle swarm optimization(PSO)algorithms to optimize the parameters of the SVM.The paper proposes a hybrid assessment model of PSO-SVM and BPNN based on dynamic weighting rules.Three sets of icing data under a rotating working state of the wind turbine were used as examples for model verification.Based on a comparative analysis with other models,the results showed that the proposed model has better accuracy and stability in analyzing the icing on wind turbine blades.

Surface Quality Evaluation of Fluff Fabric Based on Particle Swarm Optimization Back Propagation Neural Network

Aiming at the problem that back propagation(BP)neural network predicts the low accuracy rate of fluff fabric after fluffing process,a BP neural network model optimized by particle swarm optimization(PSO)algorithm is proposed.The sliced image is obtained by the principle of light-cutting imaging.The fluffy region of the adaptive image segmentation is extracted by the Freeman chain code principle.The upper edge coordinate information of the fabric is subjected to one-dimensional discrete wavelet decomposition to obtain high frequency information and low frequency information.After comparison and analysis,the BP neural network was trained by high frequency information,and the PSO algorithm was used to optimize the BP neural network.The optimized BP neural network has better weights and thresholds.The experimental results show that the accuracy of the optimized BP neural network after applying high-frequency information training is 97.96%,which is 3.79%higher than that of the unoptimized BP neural network,and has higher detection accuracy.

Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material

In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural net- works. Based on cases related to our test data of filling material, the predicted results of the model and measured values are com- pared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries.However,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP)problem.Considering the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP)strategy is used to solve the problem.This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model,and a relaxed MILP model is constructed on this basis.The particle swarm optimization algorithm with niche technology(NPSO)is proposed to optimize the solution,and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes,the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results,thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications.The optimization result of the MILP model provides a good initial point.By fixing the integer variables to the MILPoptimal value,the approximate MINLP optimal solution can be obtained through a NLP solution.The above solution strategy has been successfully applied to the actual gasoline production case of a refinery(3.5 million tons per year),and the results show that the strategy is effective and feasible.The optimization results based on the closed-loop scheduling optimization strategy have higher reliability.Compared with the standard particle swarm optimization algorithm,NPSO algorithm improves the optimization ability and efficiency to a certain extent,effectively reduces the blending cost while ensuring the convergence speed.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of a classic model of literature for fiber efficiency, which is numerically solved along with the convection diffusion equation in polar coordinates for particle concentration, with associated initial and boundary conditions. A sufficient number of examples from two numerical simulations are employed to construct a database, from which parameters of a novel neural model are adjusted. This model is constructed based on the back propagation algorithm in order to map two features, namely Peclet number and packing density, which are extracted from the numerical simulations into the corresponding single fiber efficiency. The results indicate that the developed neural model can be trained in a reasonable computational time and is capable of estimating single fiber efficiency from examples of the test set with a maximum error of 1.7%.

Auto recognition of carbonate microfacies based on an improved back propagation neural network

Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.

Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

基于不同算法优化的back propagation神经网络在三元乙丙橡胶混炼胶门尼黏度预测中的应用

分别采用遗传算法(GA)和粒子群算法(PSO)优化的back propagation(BP)神经网络建立了三元乙丙橡胶(EPDM)混炼胶门尼黏度的预测模型,并对预测结果的误差进行了对比分析。结果表明,两种算法优化后的BP神经网络模型的预测值与实测值均保持较高的拟合度和相关性;相比单一的BP神经网络,GA优化后BP神经网络模型的精度提高了58.9%,PSO优化后BP神经网络模型的精度提高了3.57%,说明两种算法优化后的预测模型,特别是GA优化的BP神经网络预测模型对EPDM混炼胶门尼黏度的预测精度改善明显。

Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization

Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

DDoS Attack Detection Scheme Based on Entropy and PSO-BP Neural Network in SDN

SDN (Software Defined Network) has many security problems, and DDoS attack is undoubtedly the most serious harm to SDN architecture network. How to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security research. Based on the characteristics of SDN, a DDoS attack detection method combining generalized entropy and PSOBP neural network is proposed. The traffic is pre-detected by the generalized entropy method deployed on the switch, and the detection result is divided into normal and abnormal. Locate the switch that issued the abnormal alarm. The controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal switch. Experiments show that compared with other methods, the detection accurate rate is guaranteed while the CPU load of the controller is reduced, and the detection capability is better.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

HPSO-based fuzzy neural network control for AUV

A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.

A Hybrid Neural Network-based Approach for Forecasting Water Demand

Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is scarce.Therefore,it is crucial to forecast water demand to provide it to sectors either on regular or emergency days.The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions.This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future.Focusing on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’hyperparameters.Based on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series forecasting.Also,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.

Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.