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

Dissolved oxygen control of activated sludge biorectors using neural-adaptive control 基于神经自适应控制的活性污泥生物反应器溶解氧控制

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013237

S. Mirghasemi, C. Macnab, A. Chu

引用次数: 12

Context-based adaptive robot behavior learning model (CARB-LM) 基于上下文的自适应机器人行为学习模型(CARB-LM)

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013253

Joohee Suh, Dean Frederick Hougen

{"title":"Context-based adaptive robot behavior learning model (CARB-LM)","authors":"Joohee Suh, Dean Frederick Hougen","doi":"10.1109/CICA.2014.7013253","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013253","url":null,"abstract":"An important, long-term objective of intelligent robotics is to develop robots that can learn about and adapt to new environments. We focus on developing a learning model that can build up new knowledge through direct experience with and feedback from an environment. We designed and constructed Context-based Adaptive Robot Behavior-Learning Model (CARB-LM) which is conceptually inspired by Hebbian and anti-Hebbian learning and by neuromodulation in neural networks. CARB-LM has two types of learning processes: (1) context-based learning and (2) reward-based learning. The former uses past accumulated positive experiences as analogies to current conditions, allowing the robot to infer likely rewarding behaviors, and the latter exploits current reward information so the robot can refine its behaviors based on current experience. The reward is acquired by checking the effect of the robot's behavior in the environment. As a first test of this model, we tasked a simulated TurtleBot robot with moving smoothly around a previously unexplored environment. We simulated this environment using ROS and Gazebo and performed experiments to evaluate the model. The robot showed substantial learning and greatly outperformed both a hand-coded controller and a randomly wandering robot.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"49 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":"115882089","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: 3

How to detect big buyers in Hong Kong stock market and follow them up to make money 如何发现港股市场的大买家并跟进赚钱

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013251

Li-Xin Wang

引用次数: 0

SOFC for TS fuzzy systems: Less conservative and local stabilization conditions TS模糊系统的SOFC:低保守性和局部镇定条件

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013233

L. Mozelli, F. O. Souza, E. Mendes

引用次数: 6

An input-output clustering approach for structure identification of T-S fuzzy neural networks 一种用于T-S模糊神经网络结构识别的输入-输出聚类方法

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013228

Wei Li, Hong-gui Han, J. Qiao

引用次数: 1

Neural network fitting for input-output manifolds of online control laws in constrained linear systems 约束线性系统在线控制律输入输出流形的神经网络拟合

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013246

Samronne N. do Carmo, M. O. D. Almeida, F. A. D. Castro, Rafael F. R. Campos, J. M. Araújo, C. Dórea

{"title":"Neural network fitting for input-output manifolds of online control laws in constrained linear systems","authors":"Samronne N. do Carmo, M. O. D. Almeida, F. A. D. Castro, Rafael F. R. Campos, J. M. Araújo, C. Dórea","doi":"10.1109/CICA.2014.7013246","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013246","url":null,"abstract":"Control techniques for systems with constraints on control and state are somewhat attractive, mainly in cases where these constraints represent safety or critical points of operation. An important approach for control of constrained linear systems is based on the concept of set invariance, whose main advantages are the inclusion of constraints in the whole design, the non-conservative nature of the controllers and the ability to cope with noise measurement and disturbance entering in the system. Some disadvantage are a possibly high complexity of the control law for higher order systems or the absence of an analytical, off-line control law in some cases, as, for instance, in the output feedback case. The online computation of the control input at each step is ever possible, but the computational cost involved may turn the solution impracticable in the case of systems with fast dynamics. Neural networks, on the other hand, is an interesting alternative for function approximation, and works well in capturing the characteristics of the input-output manifold of the online control law, starting from a training set generated by simulation of the control system. In this paper, neural networks are applied to substitute in an efficient way the online control computation. A real case based example is used to verify the effectiveness of the proposed neural controller.","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":"123118115","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

An efficient method to evaluate the performance of edge detection techniques by a two-dimensional Semi-Markov model 基于二维半马尔可夫模型的边缘检测技术性能评价方法

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013248

D. Dubinin, V. Geringer, A. Kochegurov, K. Reif

引用次数: 6

Estimation of states of a nonlinear plant using dynamic neural network 基于动态神经网络的非线性对象状态估计

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013238

A. K. Deb, D. Guha

引用次数: 0

Cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks 基于关联函数和神经网络的级联自由搜索差分进化算法在非线性系统辨识中的应用

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013239

H. V. Ayala, L. F. D. Cruz, R. Z. Freire, L. Coelho

引用次数: 7

Extreme learning ANFIS for control applications 用于控制应用的极限学习ANFIS

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2014-12-01 DOI: 10.1109/CICA.2014.7013226

G. Pillai, Pushpak Jagtap, M. Nisha

引用次数: 27