2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)最新文献_第2页

What happens when trend-followers and contrarians interplay in stock market 当趋势跟随者和逆势者在股市中相互作用时会发生什么

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013247

Li-Xin Wang

引用次数: 0

Design and implementation of a robust fuzzy controller for a rotary inverted pendulum using the Takagi-Sugeno descriptor representation 基于Takagi-Sugeno描述子的旋转倒立摆鲁棒模糊控制器的设计与实现

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013249

Q. Dang, Benyamine Allouche, L. Vermeiren, A. Dequidt, M. Dambrine

引用次数: 16

Ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator 用于控制信号发生器的超高频多项式和正弦人工高阶神经网络

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013235

Ming Zhang

引用次数: 1

Robust pinning control of complex dynamical networks using recurrent neural networks 基于递归神经网络的复杂动态网络鲁棒固定控制

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013236

E. Sánchez, D. Rodriguez-Castellanos

引用次数: 4

Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers 具有聚类中心映射的Mamdani型模糊推理系统的协同模糊规则学习

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013227

M. Prasad, Kuang-Pen Chou, A. Saxena, Omprakash Kaiwartya, Dong-Lin Li, Chin-Teng Lin

引用次数: 11

Biomimetic hybrid feedback feedforword adaptive neural control of robotic arms 机械臂仿生混合反馈前馈自适应神经控制

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013254

Yongping Pan, Haoyong Yu

{"title":"Biomimetic hybrid feedback feedforword adaptive neural control of robotic arms","authors":"Yongping Pan, Haoyong Yu","doi":"10.1109/CICA.2014.7013254","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013254","url":null,"abstract":"This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis. The major difference of the proposed design from the traditional feedback adaptive approximation-based control (AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as NN inputs. Such a slight difference leads to several attractive properties, including the convenient NN design, the decrease of the number of NN inputs, and semiglobal asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach has two unique features: 1) all above attractive properties are achieved by a much simpler control scheme; 2) the bounds of plant uncertainties are not required to be known. Simulation results have verified the effectiveness and superiority of this approach.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123997114","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: 7

Real-time nonlinear modeling of a twin rotor MIMO system using evolving neuro-fuzzy network 基于演化神经模糊网络的双转子MIMO系统实时非线性建模

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013229

Alisson Marques da Silva, W. Caminhas, A. Lemos, F. Gomide

引用次数: 20

Optimal robust control for generalized fuzzy dynamical systems: A novel use on fuzzy uncertainties 广义模糊动力系统的最优鲁棒控制:模糊不确定性的新应用

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013232

Jin Huang, Jiaguang Sun, Xibin Zhao, M. Gu

引用次数: 0

Glucose level regulation for diabetes mellitus type 1 patients using FPGA neural inverse optimal control 应用FPGA神经逆最优控制调节1型糖尿病患者血糖水平

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013245

Jorge C. Romero-Aragon, E. Sánchez, A. Alanis

引用次数: 7

New multiagent coordination optimization algorithms for mixed-binary nonlinear programming with control applications 具有控制应用的混合二元非线性规划新多智能体协调优化算法

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013243

Haopeng Zhang, Qing Hui

{"title":"New multiagent coordination optimization algorithms for mixed-binary nonlinear programming with control applications","authors":"Haopeng Zhang, Qing Hui","doi":"10.1109/CICA.2014.7013243","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013243","url":null,"abstract":"Mixed-binary nonlinear programming (MBNP), which can be used to optimize network structure and network parameters simultaneously, has been seen widely in applications of cyber-physical network systems. However, it is quite challenging to develop efficient algorithms to solve it practically. On the other hand, swarm intelligence based optimization algorithms can simulate the cooperation and interaction behaviors from social or nature phenomena to solve complex, nonconvex nonlinear problems with high efficiency. Hence, motivated by this observation, we propose a class of new computationally efficient algorithms called coupled spring forced multiagent coordination optimization (CSFMCO), by exploiting the chaos-like behavior of two-mass two-spring mechanical systems to improve the ability of algorithmic exploration and thus to fast solve the MBNP problem. Together with the continuous version of CSFMCO, a binary version of CSFMCO and a switching version between continuous and binary versions are presented. Moreover, to numerically illustrate our proposed algorithms, a formation control problem and resource allocation problem for cyber-physical networks are investigated by using the proposed algorithms.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967057","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: 1