IEEE transactions on neural networks最新文献_第2页

Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. 基于生物启发神经网络的未知环境下多机器人实时协同狩猎。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-10-25 DOI: 10.1109/TNN.2011.2169808

Jianjun Ni, Simon X Yang

{"title":"Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments.","authors":"Jianjun Ni, Simon X Yang","doi":"10.1109/TNN.2011.2169808","DOIUrl":"https://doi.org/10.1109/TNN.2011.2169808","url":null,"abstract":"Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":" ","pages":"2062-77"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2169808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40117951","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}

引用次数: 114

Generalized constraint neural network regression model subject to linear priors. 线性先验下广义约束神经网络回归模型。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-09-29 DOI: 10.1109/TNN.2011.2167348

Ya-Jun Qu, Bao-Gang Hu

{"title":"Generalized constraint neural network regression model subject to linear priors.","authors":"Ya-Jun Qu, Bao-Gang Hu","doi":"10.1109/TNN.2011.2167348","DOIUrl":"https://doi.org/10.1109/TNN.2011.2167348","url":null,"abstract":"This paper is reports an extension of our previous investigations on adding transparency to neural networks. We focus on a class of linear priors (LPs), such as symmetry, ranking list, boundary, monotonicity, etc., which represent either linear-equality or linear-inequality priors. A generalized constraint neural network-LPs (GCNN-LPs) model is studied. Unlike other existing modeling approaches, the GCNN-LP model exhibits its advantages. First, any LP is embedded by an explicitly structural mode, which may add a higher degree of transparency than using a pure algorithm mode. Second, a direct elimination and least squares approach is adopted to study the model, which produces better performances in both accuracy and computational cost over the Lagrange multiplier techniques in experiments. Specific attention is paid to both \"hard (strictly satisfied)\" and \"soft (weakly satisfied)\" constraints for regression problems. Numerical investigations are made on synthetic examples as well as on the real-world datasets. Simulation results demonstrate the effectiveness of the proposed modeling approach in comparison with other existing approaches.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 12","pages":"2447-59"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2167348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30181109","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}

引用次数: 45

Auto-regressive processes explained by self-organized maps. Application to the detection of abnormal behavior in industrial processes. 由自组织映射解释的自回归过程。应用于工业过程异常行为的检测。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-10-10 DOI: 10.1109/TNN.2011.2169810

Chiara Brighenti, Miguel Á Sanz-Bobi

引用次数: 22

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control. 基于数据的虚拟未建模动态驱动多变量非线性自适应切换控制。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-11-16 DOI: 10.1109/TNN.2011.2167685

Tianyou Chai, Yajun Zhang, Hong Wang, Chun-Yi Su, Jing Sun

{"title":"Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.","authors":"Tianyou Chai, Yajun Zhang, Hong Wang, Chun-Yi Su, Jing Sun","doi":"10.1109/TNN.2011.2167685","DOIUrl":"https://doi.org/10.1109/TNN.2011.2167685","url":null,"abstract":"For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 12","pages":"2154-72"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2167685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30272517","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}

引用次数: 55

Data-driven modeling based on volterra series for multidimensional blast furnace system. 基于volterra序列的多维高炉系统数据驱动建模。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-11-23 DOI: 10.1109/TNN.2011.2175945

Chuanhou Gao, Ling Jian, Xueyi Liu, Jiming Chen, Youxian Sun

{"title":"Data-driven modeling based on volterra series for multidimensional blast furnace system.","authors":"Chuanhou Gao, Ling Jian, Xueyi Liu, Jiming Chen, Youxian Sun","doi":"10.1109/TNN.2011.2175945","DOIUrl":"https://doi.org/10.1109/TNN.2011.2175945","url":null,"abstract":"The multidimensional blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction and blast furnace automation. For this reason, this paper is concerned with developing data-driven models based on the Volterra series for this complex system. Three kinds of different low-order Volterra filters are designed to predict the hot metal silicon content collected from a pint-sized blast furnace, in which a sliding window technique is used to update the filter kernels timely. The predictive results indicate that the linear Volterra predictor can describe the evolvement of the studied silicon sequence effectively with the high percentage of hitting the target, very low root mean square error and satisfactory confidence level about the reliability of the future prediction. These advantages and the low computational complexity reveal that the sliding-window linear Volterra filter is full of potential for multidimensional blast furnace system. Also, the lack of the constructed Volterra models is analyzed and the possible direction of future investigation is pointed out.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 12","pages":"2272-83"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2175945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30290087","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}

引用次数: 33

Guest editorial: special section on data-based control, modeling, and optimization. 客座编辑:关于基于数据的控制、建模和优化的特别部分。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-12-07 DOI: 10.1109/TNN.2011.2177733

Tianyou Chai, Zhongsheng Hou, Frank L Lewis, Amir Hussain, Dongbin Zhao

引用次数: 21

Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition. BCM脉冲神经网络的活动依赖可塑性建模及其在人类行为识别中的应用。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-10-20 DOI: 10.1109/TNN.2011.2171044

Yan Meng, Yaochu Jin, Jun Yin

{"title":"Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition.","authors":"Yan Meng, Yaochu Jin, Jun Yin","doi":"10.1109/TNN.2011.2171044","DOIUrl":"https://doi.org/10.1109/TNN.2011.2171044","url":null,"abstract":"Spiking neural networks (SNNs) are considered to be computationally more powerful than conventional NNs. However, the capability of SNNs in solving complex real-world problems remains to be demonstrated. In this paper, we propose a substantial extension of the Bienenstock, Cooper, and Munro (BCM) SNN model, in which the plasticity parameters are regulated by a gene regulatory network (GRN). Meanwhile, the dynamics of the GRN is dependent on the activation levels of the BCM neurons. We term the whole model \"GRN-BCM.\" To demonstrate its computational power, we first compare the GRN-BCM with a standard BCM, a hidden Markov model, and a reservoir computing model on a complex time series classification problem. Simulation results indicate that the GRN-BCM significantly outperforms the compared models. The GRN-BCM is then applied to two widely used datasets for human behavior recognition. Comparative results on the two datasets suggest that the GRN-BCM is very promising for human behavior recognition, although the current experiments are still limited to the scenarios in which only one object is moving in the considered video sequences.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 12","pages":"1952-66"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2171044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30233685","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}

引用次数: 31

Data-based controllability and observability analysis of linear discrete-time systems. 基于数据的线性离散系统的可控性和可观测性分析。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-11-18 DOI: 10.1109/TNN.2011.2170219

Zhuo Wang, Derong Liu

引用次数: 58

Reduced-size kernel models for nonlinear hybrid system identification. 非线性混合系统辨识的缩减核模型。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-10-25 DOI: 10.1109/TNN.2011.2171361

Van Luong Le, Grard Bloch, Fabien Lauer

{"title":"Reduced-size kernel models for nonlinear hybrid system identification.","authors":"Van Luong Le, Grard Bloch, Fabien Lauer","doi":"10.1109/TNN.2011.2171361","DOIUrl":"https://doi.org/10.1109/TNN.2011.2171361","url":null,"abstract":"This brief paper focuses on the identification of nonlinear hybrid dynamical systems, i.e., systems switching between multiple nonlinear dynamical behaviors. Thus the aim is to learn an ensemble of submodels from a single set of input-output data in a regression setting with no prior knowledge on the grouping of the data points into similar behaviors. To be able to approximate arbitrary nonlinearities, kernel submodels are considered. However, in order to maintain efficiency when applying the method to large data sets, a preprocessing step is required in order to fix the submodel sizes and limit the number of optimization variables. This brief paper proposes four approaches, respectively inspired by the fixed-size least-squares support vector machines, the feature vector selection method, the kernel principal component regression and a modification of the latter, in order to deal with this issue and build sparse kernel submodels. These are compared in numerical experiments, which show that the proposed approach achieves the simultaneous classification of data points and approximation of the nonlinear behaviors in an efficient and accurate manner.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":" ","pages":"2398-405"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2171361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40117953","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}

引用次数: 37

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. 一类MIMO非线性离散系统的数据驱动无模型自适应控制。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-11-30 DOI: 10.1109/TNN.2011.2176141

Zhongsheng Hou, Shangtai Jin

引用次数: 445