Radar Working State Recognition Based on Improved HPSO-BP

In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

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A MSVM Quality Pattern Recognition Model for Dynamic Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.555 ◽

2013 ◽

Vol 433-435 ◽

pp. 555-561

Author(s):

Yu Min Liu ◽

Hao Fei Zhou ◽

Shuai Zhang

Keyword(s):

Pattern Recognition ◽

Dynamic Process ◽

Simulation Experiment ◽

Recognition Accuracy ◽

Kernel Functions ◽

Automatic Production ◽

Recognition Model ◽

Online Quality Control ◽

Recognition Quality ◽

Abnormal Pattern

Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition model of quality pattern in dynamic process using multi-kernel function according to the experiment results.

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A Modified PSO-BP Algorithm in Hydraulic System Fault Diagnosis Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1145 ◽

2012 ◽

Vol 182-183 ◽

pp. 1145-1148 ◽

Cited By ~ 1

Author(s):

Zeng Shou Dong ◽

Xiao Yu Zhang ◽

Jian Chao Zeng

Keyword(s):

Fault Diagnosis ◽

Convergence Rate ◽

Hydraulic System ◽

Particle Swarm ◽

Bp Algorithm ◽

Particle Swarm Algorithm ◽

Bp Network ◽

Learning Factor ◽

Swarm Algorithm ◽

Modified Pso

BP neural network for failure pattern recognition has been used in hydraulic system fault diagnosis.However, its convergence rate is relatively small and always trapped at the local minima. So a new modified PSO-BP hydraulic system fault diagnosis method was proposed,which combined the respective advantages of particle swarm algorithm and BP algorithm. Firstly, the inertia weight and learning factor of the standard particle swarm algorithm was improved, then BP neural network’s weights and thresholds were optimized by modified PSO algorithm. BP network performance was ameliorated. The simulation results showed that this method improved the convergence rate of the BP network, and it could reduce the diagnostic errors.

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Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2021100106 ◽

2021 ◽

Vol 12 (4) ◽

pp. 98-116

Author(s):

Noureddine Boukhari ◽

Fatima Debbat ◽

Nicolas Monmarché ◽

Mohamed Slimane

Keyword(s):

Convergence Rate ◽

Optimization Problem ◽

Optimization Problems ◽

State Of The Art ◽

Hybrid Approach ◽

Optimization Methods ◽

Global Optimum ◽

Stochastic Methods ◽

Local Optima ◽

Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

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Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽

10.3390/e20090701 ◽

2018 ◽

Vol 20 (9) ◽

pp. 701 ◽

Cited By ~ 6

Author(s):

Beige Ye ◽

Taorong Qiu ◽

Xiaoming Bai ◽

Ping Liu

Keyword(s):

Principal Component Analysis ◽

Recognition Accuracy ◽

Principal Component ◽

Component Analysis ◽

Sample Entropy ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Recognition Method ◽

State Recognition ◽

Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

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Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽

10.1155/2020/8873315 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dongsheng Wang ◽

Jun Feng ◽

Xinpeng Zhao ◽

Yeping Bai ◽

Yujie Wang ◽

...

Keyword(s):

Deep Learning ◽

Cement Mortar ◽

Recognition Accuracy ◽

Image Data ◽

Concrete Lining ◽

Learning Method ◽

Recognition Method ◽

Data Set ◽

Recognition Model ◽

High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

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Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

International Journal of Antennas and Propagation ◽

10.1155/2016/2749035 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Chao Yang ◽

Jian-Ke Zhang ◽

Li-Xin Guo

Keyword(s):

Convergence Rate ◽

Artificial Bee Colony ◽

Optimization Method ◽

Research Field ◽

Invasive Weed ◽

Local Optima ◽

Abc Algorithm ◽

Bee Colony ◽

Opposition Based Learning ◽

Atmospheric Duct

The artificial bee colony (ABC) algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL) and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC) technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO) and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

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Radar Working State Recognition Based on the Unsupervised and Incremental Method

Journal of Sensors ◽

10.1155/2021/8673046 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Huiqin Li ◽

Yanling Li ◽

Chuan He ◽

Hui Zhang ◽

Jianwei Zhan

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Nearest Neighbor ◽

Pso Algorithm ◽

Radar Data ◽

Damping Factor ◽

Data Sets ◽

State Recognition ◽

Recognition Model ◽

Incremental Recognition

Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the K -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and K -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and F -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.

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Recognition of Crop Diseases Based on Depthwise Separable Convolution in Edge Computing

Sensors ◽

10.3390/s20154091 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4091

Author(s):

Musong Gu ◽

Kuan-Ching Li ◽

Zhongwen Li ◽

Qiyi Han ◽

Wenjie Fan

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Recognition Accuracy ◽

Recognition Algorithm ◽

Edge Computing ◽

Recognition Model ◽

Fast Recognition ◽

Proposed Model ◽

Crop Diseases ◽

High Recognition Accuracy

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

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Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Shock and Vibration ◽

10.1155/2019/7386523 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Yang Hui ◽

Xuesong Mei ◽

Gedong Jiang ◽

Tao Tao ◽

Changyu Pei ◽

...

Keyword(s):

Tool Wear ◽

Frequency Domain ◽

Recognition Accuracy ◽

Milling Process ◽

Ensemble Model ◽

Vibration Signals ◽

State Recognition ◽

Model Based ◽

Milling Tool ◽

Accuracy And Stability

Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

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Opposition-Based Animal Migration Optimization

Mathematical Problems in Engineering ◽

10.1155/2013/308250 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Yi Cao ◽

Xiangtao Li ◽

Jianan Wang

Keyword(s):

Convergence Rate ◽

Optimization Algorithm ◽

Experimental Verification ◽

Search Space ◽

Migration Behavior ◽

Local Optima ◽

Opposition Based Learning ◽

Animal Migration Optimization ◽

Accelerate Convergence ◽

Animal Migration

AMO is a simple and efficient optimization algorithm which is inspired by animal migration behavior. However, as most optimization algorithms, it suffers from premature convergence and often falls into local optima. This paper presents an opposition-based AMO algorithm. It employs opposition-based learning for population initialization and evolution to enlarge the search space, accelerate convergence rate, and improve search ability. A set of well-known benchmark functions is employed for experimental verification, and the results show clearly that opposition-based learning can improve the performance of AMO.

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