Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885895
Weijie Ren, Min Han, Jun Wang, Danxue Wang, Tieshan Li
Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.
{"title":"Efficient feature extraction framework for EEG signals classification","authors":"Weijie Ren, Min Han, Jun Wang, Danxue Wang, Tieshan Li","doi":"10.1109/ICICIP.2016.7885895","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885895","url":null,"abstract":"Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116240645","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885888
Qinglai Wei, Derong Liu, Ruizhuo Song
This paper is concerned with optimal control problems of discrete-time nonlinear systems via a novel Q-learning algorithm. In the newly developed Q-learning algorithm, the iterative Q function in each iteration is required to update on the whole state and control spaces, instead of being updated by a single state and control pair. A new convergence criterion of the corresponding Q-learning algorithm is presented, where the traditional constraints for the learning rates of Q-learning algorithms is relaxed. Finally, simulation results are provided to exemplify the good performance of the developed algorithm.
{"title":"Discrete-time optimal control scheme based on Q-learning algorithm","authors":"Qinglai Wei, Derong Liu, Ruizhuo Song","doi":"10.1109/ICICIP.2016.7885888","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885888","url":null,"abstract":"This paper is concerned with optimal control problems of discrete-time nonlinear systems via a novel Q-learning algorithm. In the newly developed Q-learning algorithm, the iterative Q function in each iteration is required to update on the whole state and control spaces, instead of being updated by a single state and control pair. A new convergence criterion of the corresponding Q-learning algorithm is presented, where the traditional constraints for the learning rates of Q-learning algorithms is relaxed. Finally, simulation results are provided to exemplify the good performance of the developed algorithm.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129766980","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885887
Kun Zhang, Huaguang Zhang, He Jiang, Chong Liu
This paper is concerned with the optimal control problem for a class of unknown continuous-time nonlinear system. A system identification method by date-driven model is established to reconstruct the unknown system dynamic by the input-output data. Then considering the optimal control problem, a novel critic neural networks design is proposed based on the policy iteration (PI), where the updating laws of parameters are designed by the normalized gradient descent algorithm and convex optimization method. And the computational burden of cost error get reduced during the iteration procedure using the new method. Based on this adaptive dynamic programming algorithm, the weight convergence is obtained and stability is guaranteed by Lyapunov theory. Finally, two simulation examples are shown to verify the effectiveness of this novel method.
{"title":"Data-driven optimal control for a class of unknown continuous-time nonlinear system using a novel ADP method","authors":"Kun Zhang, Huaguang Zhang, He Jiang, Chong Liu","doi":"10.1109/ICICIP.2016.7885887","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885887","url":null,"abstract":"This paper is concerned with the optimal control problem for a class of unknown continuous-time nonlinear system. A system identification method by date-driven model is established to reconstruct the unknown system dynamic by the input-output data. Then considering the optimal control problem, a novel critic neural networks design is proposed based on the policy iteration (PI), where the updating laws of parameters are designed by the normalized gradient descent algorithm and convex optimization method. And the computational burden of cost error get reduced during the iteration procedure using the new method. Based on this adaptive dynamic programming algorithm, the weight convergence is obtained and stability is guaranteed by Lyapunov theory. Finally, two simulation examples are shown to verify the effectiveness of this novel method.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123493310","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885879
Kirill Filianin, S. Reinikainen, T. Sainio
To further advance existing laboratory studies, the influence of different process parameters onto current efficiency was evaluated based on real industrial process history data obtained from conventional electrowinning circuit. Multivariate calibration model under partial least squares algorithm was applied to predict current efficiency in the process. The basic model was developed using values of electrolyte cupric and ferric concentrations, and total current applied. Pairwise interaction of parameters and moving average technique were applied to improve the prediction ability of the calibration. However, model construction based on the entire data set appeared to be unreliable due to high unexplained variance in the target variable, as sensor data were daily averaged. According to cluster analysis and further Monte-Carlo simulation, the phenomena of current inefficiency causing variation in the prediction of current efficiency appeared to be of random nature, i.e. daily averaging brought random variation to the multivariate model. For this reason, the data set was analyzed with multivariate process control charts to reveal the most important samples for predictive control. Multivariate calibration model was obtained using 58 samples, while the original data set contained 214 observations. Using the model, current efficiency values can be predicted on-line based on process sensor data. Multivariate process control tool was proposed in order to effectively monitor electrowinning process and detect current inefficiencies based on direct comparison of predicted and measured values of current efficiency.
{"title":"Detection of current inefficiencies in copper electrowinning with multivariate data analysis","authors":"Kirill Filianin, S. Reinikainen, T. Sainio","doi":"10.1109/ICICIP.2016.7885879","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885879","url":null,"abstract":"To further advance existing laboratory studies, the influence of different process parameters onto current efficiency was evaluated based on real industrial process history data obtained from conventional electrowinning circuit. Multivariate calibration model under partial least squares algorithm was applied to predict current efficiency in the process. The basic model was developed using values of electrolyte cupric and ferric concentrations, and total current applied. Pairwise interaction of parameters and moving average technique were applied to improve the prediction ability of the calibration. However, model construction based on the entire data set appeared to be unreliable due to high unexplained variance in the target variable, as sensor data were daily averaged. According to cluster analysis and further Monte-Carlo simulation, the phenomena of current inefficiency causing variation in the prediction of current efficiency appeared to be of random nature, i.e. daily averaging brought random variation to the multivariate model. For this reason, the data set was analyzed with multivariate process control charts to reveal the most important samples for predictive control. Multivariate calibration model was obtained using 58 samples, while the original data set contained 214 observations. Using the model, current efficiency values can be predicted on-line based on process sensor data. Multivariate process control tool was proposed in order to effectively monitor electrowinning process and detect current inefficiencies based on direct comparison of predicted and measured values of current efficiency.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133841085","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885880
Kirill Filianin, S. Reinikainen, T. Sainio, H. Helaakoski, Vesa Kyllönen
Once a multivariate model is developed, it can be combined with tools and techniques from univariate statistical process control to form multivariate statistical process control tools. It allows development of advanced process monitoring strategies. In the current study, copper plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model was based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. Normal operating conditions were defined through control limits that were assigned to Hotelling T2 values on x-axis and to squared prediction error values on y-axis. Samples that were beyond the limits were classified as either systematic or random errors, or outliers. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional univariate techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure summarizing information from all process variables simultaneously. The proposed methodology was combined with on-line quality monitoring tool developed by VTT, Technical Research Center of Finland, to visualize the results. Thus, the proposed approach has a potential in on-line industrial instrumentation providing fast, robust and cheap application with automation abilities.
{"title":"Detection of abnormal process behavior in copper solvent extraction by Hotelling T2 and squared prediction error control chart","authors":"Kirill Filianin, S. Reinikainen, T. Sainio, H. Helaakoski, Vesa Kyllönen","doi":"10.1109/ICICIP.2016.7885880","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885880","url":null,"abstract":"Once a multivariate model is developed, it can be combined with tools and techniques from univariate statistical process control to form multivariate statistical process control tools. It allows development of advanced process monitoring strategies. In the current study, copper plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model was based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. Normal operating conditions were defined through control limits that were assigned to Hotelling T2 values on x-axis and to squared prediction error values on y-axis. Samples that were beyond the limits were classified as either systematic or random errors, or outliers. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional univariate techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure summarizing information from all process variables simultaneously. The proposed methodology was combined with on-line quality monitoring tool developed by VTT, Technical Research Center of Finland, to visualize the results. Thus, the proposed approach has a potential in on-line industrial instrumentation providing fast, robust and cheap application with automation abilities.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114412798","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885875
Y. Yoshida
Using fuzzy random variables, a dynamic portfolio model with uncertainty is mentioned for object system. In this approach, the random property is numerated by stochastic expectation and the fuzzy property is also numerated by weights and mean functions. A value-at-risk is introduced to assess the risk of unfavorable paths in investment. Using dynamic programming and mathematical programming, the optimal solutions of a dynamic portfolio problem with VaR is mentioned. An optimization equation is derived and the optimal portfolios are given at each period.
{"title":"Dynamic optimization of value-at-risk portfolios with fuzziness in asset management","authors":"Y. Yoshida","doi":"10.1109/ICICIP.2016.7885875","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885875","url":null,"abstract":"Using fuzzy random variables, a dynamic portfolio model with uncertainty is mentioned for object system. In this approach, the random property is numerated by stochastic expectation and the fuzzy property is also numerated by weights and mean functions. A value-at-risk is introduced to assess the risk of unfavorable paths in investment. Using dynamic programming and mathematical programming, the optimal solutions of a dynamic portfolio problem with VaR is mentioned. An optimization equation is derived and the optimal portfolios are given at each period.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134600158","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885901
Chong Liu, Huaguang Zhang, Xianshuang Yao, Kun Zhang
In this paper, a novel model, named double-reservoir echo state networks (DR-ESN), is proposed. DR-ESN is constructed by two reservoirs which are connected in series, thus the performance of abstracting the characteristics from the prediction task is improved. A sufficient condition is provided to ensure the stability of DR-ESN. The batch gradient method and ridge regression method are utilized to optimize the six parameters of DR-ESN and train the readouts, respectively. DR-ESN is verified by two different experiments, chaotic time series prediction and real-valued function time series prediction. The simulation results demonstrates that DR-ESN has a more precise result than leaky-ESN in predicting the time series.
{"title":"Echo state networks with double-reservoir for time-series prediction","authors":"Chong Liu, Huaguang Zhang, Xianshuang Yao, Kun Zhang","doi":"10.1109/ICICIP.2016.7885901","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885901","url":null,"abstract":"In this paper, a novel model, named double-reservoir echo state networks (DR-ESN), is proposed. DR-ESN is constructed by two reservoirs which are connected in series, thus the performance of abstracting the characteristics from the prediction task is improved. A sufficient condition is provided to ensure the stability of DR-ESN. The batch gradient method and ridge regression method are utilized to optimize the six parameters of DR-ESN and train the readouts, respectively. DR-ESN is verified by two different experiments, chaotic time series prediction and real-valued function time series prediction. The simulation results demonstrates that DR-ESN has a more precise result than leaky-ESN in predicting the time series.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995122","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885911
Xueheng Mei, Xin Li, Hao Su, B. Cai, Lixian Zhang
The paper focuses on the H∞ fault detection problem for a class of networked systems with intermittent measurements. The fault detection filter (FDF) design is formulated as an H∞ filtering problem by using a FDF. The random packet dropouts, which are described by a Bernoulli distributed sequence, are considered to exist in the communication channels. The packet dropout rate (PDR) is uncertain and variable, which is described by a Markov stochastic process. Based on mode-dependent Lyapunov function, sufficient conditions on the existence of a desired FDF are presented such that the filtering error system is stochastically mean-square stable with a prescribed H∞ disturbance attenuation level. Finally, an illustrative example is provided to demonstrate the effectiveness of the designed filter and the necessity of taking the uncertainty and variation of PDR into account in the design process.
{"title":"Fault detection for networked systems with variable packet dropout rate","authors":"Xueheng Mei, Xin Li, Hao Su, B. Cai, Lixian Zhang","doi":"10.1109/ICICIP.2016.7885911","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885911","url":null,"abstract":"The paper focuses on the H∞ fault detection problem for a class of networked systems with intermittent measurements. The fault detection filter (FDF) design is formulated as an H∞ filtering problem by using a FDF. The random packet dropouts, which are described by a Bernoulli distributed sequence, are considered to exist in the communication channels. The packet dropout rate (PDR) is uncertain and variable, which is described by a Markov stochastic process. Based on mode-dependent Lyapunov function, sufficient conditions on the existence of a desired FDF are presented such that the filtering error system is stochastically mean-square stable with a prescribed H∞ disturbance attenuation level. Finally, an illustrative example is provided to demonstrate the effectiveness of the designed filter and the necessity of taking the uncertainty and variation of PDR into account in the design process.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127263344","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885889
Xing Huo, A. Zhang, Zhiqiang Zhang, Zhiyong She
A nonlinear control approach for satellite attitude stabilization maneuver is presented. The controller is developed by using backstepping control technique. The non-ideal dynamical behavior of actuators, referred to as actuator dynamics, is investigated. The satellite s attitude is described by MRPs. The satellites dynamic model can be deduced by a general model of actuator dynamics. And this general model actuator can be expressed all actuators possibly for space application. External disturbances and actuator constraints are all considered during this simulation. Simulation results revealed the control validity of the proposed controller.
{"title":"A attitude control method for spacecraft considering actuator constraint and dynamics based backstepping","authors":"Xing Huo, A. Zhang, Zhiqiang Zhang, Zhiyong She","doi":"10.1109/ICICIP.2016.7885889","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885889","url":null,"abstract":"A nonlinear control approach for satellite attitude stabilization maneuver is presented. The controller is developed by using backstepping control technique. The non-ideal dynamical behavior of actuators, referred to as actuator dynamics, is investigated. The satellite s attitude is described by MRPs. The satellites dynamic model can be deduced by a general model of actuator dynamics. And this general model actuator can be expressed all actuators possibly for space application. External disturbances and actuator constraints are all considered during this simulation. Simulation results revealed the control validity of the proposed controller.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122436972","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885913
Genghang Chen, An Song, Chun-Ju Zhang, Xiao-Fang Liu, Wei-neng Chen, Zhi-hui Zhan, J. Zhong, Jun Zhang, Xiao-Min Hu
Recently, partitional clustering approaches based on Evolutionary Algorithms (EAs) have shown promising in solving the data clustering problems. However, with the nearest prototype (NP) rule as the method for decoding, most of them are only suitable for clustering datasets with convex (e.g. hyperspherical) clusters. In this paper, we propose an automatic clustering approach using particle swarm optimization (PSO). A new encoding scheme with a novel decoding method, named the nearest multiple prototypes (NMP) rule, is applied to the PSO-based clustering algorithm to automatically determine an appropriate number of clusters in the procedure of clustering and partition datasets with arbitrary shaped clusters. The algorithm is experimentally validated on both synthetic and real datasets. The results show that the proposed PSO-based approach is very competitive when comparing with two popular clustering algorithms.
{"title":"Automatic clustering approach based on particle swarm optimization for data with arbitrary shaped clusters","authors":"Genghang Chen, An Song, Chun-Ju Zhang, Xiao-Fang Liu, Wei-neng Chen, Zhi-hui Zhan, J. Zhong, Jun Zhang, Xiao-Min Hu","doi":"10.1109/ICICIP.2016.7885913","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885913","url":null,"abstract":"Recently, partitional clustering approaches based on Evolutionary Algorithms (EAs) have shown promising in solving the data clustering problems. However, with the nearest prototype (NP) rule as the method for decoding, most of them are only suitable for clustering datasets with convex (e.g. hyperspherical) clusters. In this paper, we propose an automatic clustering approach using particle swarm optimization (PSO). A new encoding scheme with a novel decoding method, named the nearest multiple prototypes (NMP) rule, is applied to the PSO-based clustering algorithm to automatically determine an appropriate number of clusters in the procedure of clustering and partition datasets with arbitrary shaped clusters. The algorithm is experimentally validated on both synthetic and real datasets. The results show that the proposed PSO-based approach is very competitive when comparing with two popular clustering algorithms.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258565","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：