Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002853
Yifei Yu, Haoran Qin, Yuanxiang Li, Zaifen Gao, Z. Gai
Epilepsy patients experience challenges in daily life, and epilepsy seizures might cause injuries or endanger the life of the patients or others. The electroencephalogram (EEG) signals, recorded by a machine or device, are often used to analyze the brain electrical activity, which is noninvasive. Locating the seizure period in EEG recordings is usually difficult and time consuming for doctors. Therefore, automatic detection of seizures is necessary. In this paper, we use the autocorrelation function to extract the EEG features, and propose a method based on Recurrent Neural Network to detect the seizure period of the EEG signal, which combines the gated recurrent unit and a 1-D convolutional embedding head. We use the clinical EEG recording of 15 patients to simulate the results of our proposed method. The experimental results demonstrate that our method achieves an excellent performance with 99.6% detection accuracy for Absence Seizure, which can greatly reduce the workload of doctors in clinical diagnosis.
{"title":"EEG Absence Seizure Detection with Autocorrelation Function and Recurrent Neural Network","authors":"Yifei Yu, Haoran Qin, Yuanxiang Li, Zaifen Gao, Z. Gai","doi":"10.1109/SSCI44817.2019.9002853","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002853","url":null,"abstract":"Epilepsy patients experience challenges in daily life, and epilepsy seizures might cause injuries or endanger the life of the patients or others. The electroencephalogram (EEG) signals, recorded by a machine or device, are often used to analyze the brain electrical activity, which is noninvasive. Locating the seizure period in EEG recordings is usually difficult and time consuming for doctors. Therefore, automatic detection of seizures is necessary. In this paper, we use the autocorrelation function to extract the EEG features, and propose a method based on Recurrent Neural Network to detect the seizure period of the EEG signal, which combines the gated recurrent unit and a 1-D convolutional embedding head. We use the clinical EEG recording of 15 patients to simulate the results of our proposed method. The experimental results demonstrate that our method achieves an excellent performance with 99.6% detection accuracy for Absence Seizure, which can greatly reduce the workload of doctors in clinical diagnosis.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"26 1","pages":"3059-3064"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84832165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9003130
Ryoya Osawa, Shinya Watanabe, T. Hiroyasu, S. Hiwa
Multimodality is often observed in practical optimization problems. Therefore, multi-modal multi-objective evolutionary algorithms (MMEA) have been developed to tackle the multimodality of these problems. However, most of the existing studies focused on population diversity in either an objective or a decision space. A double-niched evolutionary algorithm (DNEA) is a state-of-the-art MMEA that employs a niche-sharing method to improve the population in both the objective and decision spaces. However, its performance has been evaluated solely for real-coded problems and not for binary-coded ones. In this study, the performance of DNEA is evaluated on a multi-objective 0/1 knapsack problem, and the population diversity in both the objective and decision spaces is evaluated using a pure diversity measure. The experimental results suggest that DNEA is effective for multi-objective 0/1 knapsack problems to improve the decision space diversity; further, its performance is significantly affected by its control parameter, niche radius.
{"title":"Performance Study of Double-Niched Evolutionary Algorithm on Multi-objective Knapsack Problems","authors":"Ryoya Osawa, Shinya Watanabe, T. Hiroyasu, S. Hiwa","doi":"10.1109/SSCI44817.2019.9003130","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003130","url":null,"abstract":"Multimodality is often observed in practical optimization problems. Therefore, multi-modal multi-objective evolutionary algorithms (MMEA) have been developed to tackle the multimodality of these problems. However, most of the existing studies focused on population diversity in either an objective or a decision space. A double-niched evolutionary algorithm (DNEA) is a state-of-the-art MMEA that employs a niche-sharing method to improve the population in both the objective and decision spaces. However, its performance has been evaluated solely for real-coded problems and not for binary-coded ones. In this study, the performance of DNEA is evaluated on a multi-objective 0/1 knapsack problem, and the population diversity in both the objective and decision spaces is evaluated using a pure diversity measure. The experimental results suggest that DNEA is effective for multi-objective 0/1 knapsack problems to improve the decision space diversity; further, its performance is significantly affected by its control parameter, niche radius.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"35 1","pages":"1793-1801"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90537710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002986
Na Liu, Han Zhang, Sitian Qin
Fuzzy optimization widely occurs in various field. In this paper, by the virtue of weighting method, the original fuzzy optimization problem is eventually converted to a single-objective form. Then, a neurodynamic approach is introduced for this problem. The state solution of the neural network is shown to enter the feasible region of the considered optimization problem in finite time and remain in the feasible region since then. Moreover, the state solution of the introduced neural network converges to an optimal solution of the considered optimization problem. In the end, a numerical example is presented to clarify the practicability of the introduced neural network.
{"title":"A Neural Network for Constrained Fuzzy Convex Optimization Problems","authors":"Na Liu, Han Zhang, Sitian Qin","doi":"10.1109/SSCI44817.2019.9002986","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002986","url":null,"abstract":"Fuzzy optimization widely occurs in various field. In this paper, by the virtue of weighting method, the original fuzzy optimization problem is eventually converted to a single-objective form. Then, a neurodynamic approach is introduced for this problem. The state solution of the neural network is shown to enter the feasible region of the considered optimization problem in finite time and remain in the feasible region since then. Moreover, the state solution of the introduced neural network converges to an optimal solution of the considered optimization problem. In the end, a numerical example is presented to clarify the practicability of the introduced neural network.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"2 1","pages":"1007-1012"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90582989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002817
L. Lu, Tingting Pan, Junhong Zhao, Jie Yang
Currently, most of works about interpretation of neural networks are to visually explain the features learned by hidden layers. This paper explores the relationship between the input units and the output units of neural network from the perspective of probability density. For classification problems, it shows that the probability density function (PDF) of the output unit can be expressed as a mixture of three Gaussian density functions whose mean and variance are related to the information of the input units, under the assumption that the input units are independent of each other and obey a Gaussian distribution. The experimental results show that the theoretical distribution of the output unit is basically consistent with the actual distribution.
{"title":"Interpretability for Neural Networks from the Perspective of Probability Density","authors":"L. Lu, Tingting Pan, Junhong Zhao, Jie Yang","doi":"10.1109/SSCI44817.2019.9002817","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002817","url":null,"abstract":"Currently, most of works about interpretation of neural networks are to visually explain the features learned by hidden layers. This paper explores the relationship between the input units and the output units of neural network from the perspective of probability density. For classification problems, it shows that the probability density function (PDF) of the output unit can be expressed as a mixture of three Gaussian density functions whose mean and variance are related to the information of the input units, under the assumption that the input units are independent of each other and obey a Gaussian distribution. The experimental results show that the theoretical distribution of the output unit is basically consistent with the actual distribution.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"282 3 1","pages":"1502-1507"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89367092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9003038
Haozhe Liang, Xiwen Gong
In order to solve the problem of fault reconstruction of the mother-connected closed grid, this paper proposes a power grid fault reconstruction strategy based on two-step binary particle swarm optimization. According to the network architecture and electrical characteristics of the deepwater semi-submarine platform power grid, a fault recovery model for the closed bus-ties grid is established. The objective function of the proposed model is to recover the important load as much as possible, and the constraints are based on the power grid structure and system capacity. In order to improve the efficiency of the solution, a two-stage optimization solution process of binary particle swarm optimization algorithm is designed for the established model, and it is used to compare with the simulation results of chaotic genetic algorithm and immune cloning algorithm. The simulation results show that the proposed strategy has better search efficiency and optimization ability, and can effectively improve the speed and accuracy of the fault recovery of the bus-ties closed power grid.
{"title":"Research on Reconstruction Strategy of Closed Bus-ties Power Grid Based on Binary Particle Swarm Optimization","authors":"Haozhe Liang, Xiwen Gong","doi":"10.1109/SSCI44817.2019.9003038","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003038","url":null,"abstract":"In order to solve the problem of fault reconstruction of the mother-connected closed grid, this paper proposes a power grid fault reconstruction strategy based on two-step binary particle swarm optimization. According to the network architecture and electrical characteristics of the deepwater semi-submarine platform power grid, a fault recovery model for the closed bus-ties grid is established. The objective function of the proposed model is to recover the important load as much as possible, and the constraints are based on the power grid structure and system capacity. In order to improve the efficiency of the solution, a two-stage optimization solution process of binary particle swarm optimization algorithm is designed for the established model, and it is used to compare with the simulation results of chaotic genetic algorithm and immune cloning algorithm. The simulation results show that the proposed strategy has better search efficiency and optimization ability, and can effectively improve the speed and accuracy of the fault recovery of the bus-ties closed power grid.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"3053-3058"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89659434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9003158
Yohei Domen, T. Migita, Norikazu Takahashi
This paper proposes a distributed algorithm for multiple agents to perform the Nonnegative Matrix Factorization (NMF) based on the Euclidean distance. The matrix to be factorized is partitioned into multiple blocks, and each block is assigned to one of the agents forming a two-dimensional grid network. Each agent handles a small number of entries of the factor matrices corresponding to the assigned block, and updates their values by using information coming from the neighbors. It is shown that the proposed algorithm simulates the hierarchical alternating least squares method, which is well known as a fast algorithm for NMF based on the Euclidean distance, by making use of a finite-time distributed consensus algorithm.
{"title":"A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization","authors":"Yohei Domen, T. Migita, Norikazu Takahashi","doi":"10.1109/SSCI44817.2019.9003158","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003158","url":null,"abstract":"This paper proposes a distributed algorithm for multiple agents to perform the Nonnegative Matrix Factorization (NMF) based on the Euclidean distance. The matrix to be factorized is partitioned into multiple blocks, and each block is assigned to one of the agents forming a two-dimensional grid network. Each agent handles a small number of entries of the factor matrices corresponding to the assigned block, and updates their values by using information coming from the neighbors. It is shown that the proposed algorithm simulates the hierarchical alternating least squares method, which is well known as a fast algorithm for NMF based on the Euclidean distance, by making use of a finite-time distributed consensus algorithm.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"6 1","pages":"1332-1337"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87218678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002862
Zhixing Huang, Chengyu Lu, J. Zhong
Monitoring dangerous regions is one of the most important applications of wireless sensor networks. Limited by the danger of monitoring regions and the battery power of sensors, unmanned aerial vehicles (UAVs) are often used to collect data in such applications. How to properly schedule the movement of UAVs to efficiently collect data is still a challenging problem to be solved. In this paper, we formulate the UAV scheduling problem as a multi-objective optimization problem and design a genetic programming based hyper-heuristic framework to solve the problem. The simulation results show that our method can provide very promising performance in comparison with several state-of-the-art methods.
{"title":"A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks","authors":"Zhixing Huang, Chengyu Lu, J. Zhong","doi":"10.1109/SSCI44817.2019.9002862","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002862","url":null,"abstract":"Monitoring dangerous regions is one of the most important applications of wireless sensor networks. Limited by the danger of monitoring regions and the battery power of sensors, unmanned aerial vehicles (UAVs) are often used to collect data in such applications. How to properly schedule the movement of UAVs to efficiently collect data is still a challenging problem to be solved. In this paper, we formulate the UAV scheduling problem as a multi-objective optimization problem and design a genetic programming based hyper-heuristic framework to solve the problem. The simulation results show that our method can provide very promising performance in comparison with several state-of-the-art methods.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"37 1","pages":"1614-1621"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87561444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002742
Yuhao Li, Jun Yu, H. Takagi
We propose a two-stage niching algorithm that separates local optima areas in the first stage and finds the optimum point of each area using any optimization technique in the second stage. The proposed first stage has complementary characteristics to the shortcoming of Nearest-better Clustering (NBC). We introduce a weighted gradient and distance-based clustering method (WGraD) and two methods for determining its weights to find out niches and overcome NBC. The WGraD creates spanning trees by connecting each search point to other suitable one decided by weighted gradient information and weighted distance information among search points. Since weights influence its clustering result, we propose two weight determination methods 1 and 2. The weight determination method 1 firstly forms one spanning tree and then uses a dynamic pruning method and the Hill-Valley test to cut long edges and repair them. The weight determination method 2 assigns different weights to different search points based on distance information. We combine these methods into WGrad, i.e. WGraD1 and WGraD2, and compare the characteristics of NBC, WGraD1, and WGraD2 using differential evolution (DE) as a baseline search algorithm for obtaining the optimum of each niche after clustering local areas. We design a controlled experiment and run (NBC + DE), (WGraD1 + DE) and (WGraD2 + DE) on 8 benchmark functions from CEC 2015 test suite for single objective multiniche optimization. The experimental results confirmed that the proposed strategy can overcome the shortcoming of NBC and be a complementary niche method of NBC.
{"title":"Niche Method Complementing the Nearest-better Clustering","authors":"Yuhao Li, Jun Yu, H. Takagi","doi":"10.1109/SSCI44817.2019.9002742","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002742","url":null,"abstract":"We propose a two-stage niching algorithm that separates local optima areas in the first stage and finds the optimum point of each area using any optimization technique in the second stage. The proposed first stage has complementary characteristics to the shortcoming of Nearest-better Clustering (NBC). We introduce a weighted gradient and distance-based clustering method (WGraD) and two methods for determining its weights to find out niches and overcome NBC. The WGraD creates spanning trees by connecting each search point to other suitable one decided by weighted gradient information and weighted distance information among search points. Since weights influence its clustering result, we propose two weight determination methods 1 and 2. The weight determination method 1 firstly forms one spanning tree and then uses a dynamic pruning method and the Hill-Valley test to cut long edges and repair them. The weight determination method 2 assigns different weights to different search points based on distance information. We combine these methods into WGrad, i.e. WGraD1 and WGraD2, and compare the characteristics of NBC, WGraD1, and WGraD2 using differential evolution (DE) as a baseline search algorithm for obtaining the optimum of each niche after clustering local areas. We design a controlled experiment and run (NBC + DE), (WGraD1 + DE) and (WGraD2 + DE) on 8 benchmark functions from CEC 2015 test suite for single objective multiniche optimization. The experimental results confirmed that the proposed strategy can overcome the shortcoming of NBC and be a complementary niche method of NBC.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"87 1","pages":"3065-3071"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88521119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/SSCI44817.2019.9003016
Min Qin, Yuhang Xi, Frank Jiang
In order to improve the accuracy of the flower image recognition, a convolutional neural network (A-LDCNN) model based on attention mechanism and LD-loss (Linear Discriminant Loss Function) is proposed. Unlike traditional CNN (Convolutional Neural Networks), A-LDCNN uses the VGG-16 network pre-trained by ImageNet to perform feature learning on preprocessed flower images. The attention feature is constructed by fusing the local features of the multiple intermediate convolution layers with the global features of the fully connected layer and using it as the final classification feature. LDA (Latent Dirichlet Allocation) is introduced into the model to construct a new loss function LD-loss, which participates in the training of CNN to minimize the feature distance in class and maximize the feature distance between classes, and to solve the problem of Inter-class similarity and intra-class difference in flower image classification. Classification experiments show that the accuracy of A-LDCNN is 87.6%, which is higher than other traditional networks and can realize the accurate recognition of flower images under natural conditions.
为了提高花图像识别的准确率，提出了一种基于注意机制和线性判别损失函数(LD-loss, Linear Discriminant Loss Function)的卷积神经网络(a - ldcnn)模型。与传统的CNN(卷积神经网络)不同，A-LDCNN使用ImageNet预训练的VGG-16网络对预处理后的花卉图像进行特征学习。将多个中间卷积层的局部特征与全连通层的全局特征融合，构建注意力特征作为最终分类特征。在模型中引入LDA (Latent Dirichlet Allocation)，构造一个新的损失函数LD-loss，参与CNN的训练，使类内特征距离最小化，类间特征距离最大化，解决花图像分类中的类间相似和类内差异问题。分类实验表明，A-LDCNN的准确率为87.6%，高于其他传统网络，可以实现自然条件下花卉图像的准确识别。
{"title":"A New Improved Convolutional Neural Network Flower Image Recognition Model","authors":"Min Qin, Yuhang Xi, Frank Jiang","doi":"10.1109/SSCI44817.2019.9003016","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003016","url":null,"abstract":"In order to improve the accuracy of the flower image recognition, a convolutional neural network (A-LDCNN) model based on attention mechanism and LD-loss (Linear Discriminant Loss Function) is proposed. Unlike traditional CNN (Convolutional Neural Networks), A-LDCNN uses the VGG-16 network pre-trained by ImageNet to perform feature learning on preprocessed flower images. The attention feature is constructed by fusing the local features of the multiple intermediate convolution layers with the global features of the fully connected layer and using it as the final classification feature. LDA (Latent Dirichlet Allocation) is introduced into the model to construct a new loss function LD-loss, which participates in the training of CNN to minimize the feature distance in class and maximize the feature distance between classes, and to solve the problem of Inter-class similarity and intra-class difference in flower image classification. Classification experiments show that the accuracy of A-LDCNN is 87.6%, which is higher than other traditional networks and can realize the accurate recognition of flower images under natural conditions.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"3110-3117"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88235835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper handles the noise-free or noisy synchronization control of Genesio chaotic (GC) system. To do so, Zhang dynamics (ZD) method is presented and exploited, and thus the ZD controllers, noise-free or noisy, are theoretically researched. Firstly, the presented ZD controller for GC system synchronization with no noise perturbation is analyzed, and the synchronization error as a whole (i.e., in the form of vector norm) between the drive GC system and the response GC system converges globally exponentially to zero. Secondly, the presented ZD controller for GC system synchronization with noise perturbation is analyzed as well, and detailed theoretical analyses (i.e., proofs) and results show that the synchronization error as a whole (i.e., in the form of vector norm) converges globally to a small bound of error. So, the ZD controllers provided in this paper (including the ones with unequal parameters) are not only simple and effective but also quite robust for the GC system synchronization.
{"title":"Noisy Zhang-Dynamics (ZD) Method for Genesio Chaotic (GC) System Synchronization: Elegant Analyses and Unequal-Parameter Extension","authors":"Canhui Chen, Yihong Ling, Deyang Zhang, Nini Shi, Yunong Zhang","doi":"10.1109/SSCI44817.2019.9002889","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002889","url":null,"abstract":"This paper handles the noise-free or noisy synchronization control of Genesio chaotic (GC) system. To do so, Zhang dynamics (ZD) method is presented and exploited, and thus the ZD controllers, noise-free or noisy, are theoretically researched. Firstly, the presented ZD controller for GC system synchronization with no noise perturbation is analyzed, and the synchronization error as a whole (i.e., in the form of vector norm) between the drive GC system and the response GC system converges globally exponentially to zero. Secondly, the presented ZD controller for GC system synchronization with noise perturbation is analyzed as well, and detailed theoretical analyses (i.e., proofs) and results show that the synchronization error as a whole (i.e., in the form of vector norm) converges globally to a small bound of error. So, the ZD controllers provided in this paper (including the ones with unequal parameters) are not only simple and effective but also quite robust for the GC system synchronization.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"79 1","pages":"482-487"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88252658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：