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International Conference on Digital Content, Multimedia technology and its Applications (IDC)

作者： Sara Manifar Mahdi Aliyari Shoorhedeli Department of Mechatronics Engineering Islamic Azad University Science and Research Tehran Iran Department of Mechatronics Engineering and Advanced Process Automation and Control (APAC) Research Group K.N. Toosi University of Technology Tehran Iran

This paper discusses the design of a controller for teleoperation systems which operate in contact with unknown environments. For this purpose, the controller in the slave part is designed to use a classical controller and a neuro-fuzzy controller. To have a haptic teleoperation, the force feedback from the contact point to the master side is needed. In this study virtual force is fed back to the master instead of the measured contact force and the feedback force is the position error i.e. the difference between the position of master and slave robots. Primary, classical controller was designed for this object, the gains of this controller is obtained by standard genetic algorithm (GA), in offline and optimization method at nominal operating point. In master-slave robot with classical controller, when, robot works in soft or hard environment and the operating point was changed, the obtained controller are not suitable. For this reason, online neuro - fuzzy gain scheduling controller are designed for this system to tune the controller's parameters. The NF trained in offline mode based on GA and in online application tuned the controller based on monitoring of operating point of system. This method makes the system robust to uncertainties such as modeling errors, parameter mismatches and disturbances. The simulation results show the ability of proposed method in compare of other controllers such as Kalman filter.

关键词： Force Equations Monitoring Iron Mathematical model

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Comparison of neuro-fuzzy approaches with artificial neural networks for the detection of Ischemia in ECG signals

Comparison of neuro-fuzzy approaches with artificial neural ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hoda Tonekabonipour Ali Emam Mohamad Teshnelab Mehdi Aliyari Shoorehdeli Mechatronics Department Qazvin Islamic Azad University Qazvin Iran Electrical Engineering K.N. Toosi University of Technology Tehran Iran Electrical Engineering Advanced Process Automation and Control (APAC) K.N. Toosi University of Technology Tehran Iran

This paper compares different classification methods of ECG signals including their accuracies. First of all , Preprocessing for ECG signal is necessary in order to detect QRS complex. Then, with the intention of extract influential features in Ischemia disease, baseline wandering and noise suppression is done. Following to above mentioned target, two neuro-fuzzy classification algorithms incorporated with two artificial neural networks classifiers selected. They put under test to investigate their ability to recognize Ischemic Heart Disease (IHD) from ECG signals. Adaptive Neuro Fuzzy Inference System (ANFIS) and Locally Linear Model Tree (LOLIMOT), are two neuro-fuzzy networks used in the test. They have good capability of learning. Also Multi layer Perceptron (MLP) and Probabilistic Neural Networks (PNN) used as well in test. These are four structures totally used in this paper. The ECG sampled signals are taken from MIT-BIH database. They are used to train neural networks enabling them to classify Ischemia. All neuro-structures have been tested by using experimental ECG records of individuals. The results suggest that neuro-fuzzy classifiers perform better than the other types of classifiers.

关键词： Electrocardiography

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Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks

Feature selection and classification of breast cancer on dyn...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Farzaneh Keivanfard Mohammad Teshnehlab Mahdi Aliyari Shoorehdeli Ke Nie Min-Ying Su Electrical and Computer Engineering Department Khaje Nasir Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department Advanced Process Automation and Control (APAC) Khaje Nasir Toosi University of Technology Tehran Iran Center for Functional Onco-Imaging and Department of Radiological Sciences University of California Irvine CA USA

In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006. A forward selection method is applied to find the best features for classification. Moreover, artificial neural networks such as Multilayer Preceptron (MLP) neural network, Probabilistic Neural Network (PNN) and Generalized Regression Neural Network (GRNN) are applied to classify breast cancer into two groups; benign and malignant lesions. Training and recalling neural networks are obtained with considering four-fold cross validation.

关键词： Fires Entropy Correlation Artificial neural networks Lesions Jacobian matrices Kinetic theory

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Sparse representation based on projection method in online least squares support vector machines

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控制理论与应用（英文版） 2009年第2期7卷 163-168页

作者： Lijuan LI Hongye SU Jian CHU State Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou Zhejiang 310027 China College of Automation Nanjing University of Technology Nanjing Jiangsu 210009 China

A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines （LS-SVM）. The new inputs are projected into the subspace spanned by previous basis vectors （BV） and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision.

关键词： Least squares support vector machines Projection Sparsity pH neutralizing process Isomerization

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Fault-tolerant fuzzy control system design with multiple indices constraints: The actuator faults case

Fault-tolerant fuzzy control system design with multiple ind...

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7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEprocess'09

作者： Han, Xiaodong Zhang, Dengfeng Xie, Dexiao Wang, Zhiquan School of Automation Nanjing University of Science and Technology Nanjing 210094 China Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9783902661463

This paper is concerned with the design of fault tolerant controllers for a class of continuous-time nonlinear systems with multiple performance criteria constraints. The Takagi and Sugeno fuzzy model is employed to represent a nonlinear system. Based on the decomposition and equivalence principle, the state feedback controllers are designed to guarantee the fuzzy closed-loop system satisfying the pre-specified regional pole index, H∞ norm-bound constraint on disturbance attenuation and having the quadratic cost performance simultaneously, in the actuator failure cases. Furthermore, the consistency of the performance indices mentioned earlier is set up for fault tolerant control. Finally, numerical simulations are given to illustrate the effectiveness of the proposed design methods. © 2009 IFAC.

关键词： Actuators

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The Effect of Tuning in Multiple-Model Adaptive controllers: A Case Study

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IFAC Proceedings Volumes 2009年第11期42卷 560-565页

作者： Ehsan Peymani Alireza Fatehi Ali Khaki Sedigh Advanced Process Automation & Control (APAC) Research Group K. N. Toosi University of Technology Tehran Iran

In this paper, two types of multiple-model adaptive controllers are practically evaluated on a laboratory-scale pH neutralization process. The first one is supervisory switching multiple-model adaptive controller (SMMAC) whose model bank is fixed and selected a priori, and another one is a controller based on multiple models, switching, and tuning strategy (MMST) which uses the possibility of model bank tuning. In addition to investigation of the effect of tuning, the advantage of a disturbance rejection supervisor is studied. Various experiments and exhaustive numerical analyses are provided to assess the abilities of the proposed algorithms.

关键词： multiple models adaptive control pH control switching control pole-placement control

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Enhancement of Multi-Objective control Performance via Switching

Enhancement of Multi-Objective Control Performance via Switc...

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2009中国控制与决策会议

作者： Laven Soltanian Ali Khaki Sedigh Omid Namaki-Shoushtari Advanced Process Automation & Control(APAC) Research Group K.N.Toosi University of TechnologyTehranIran Control Engineering Department K.N.Toosi University of TechnologyTehranIran

control system design for a complex system encompasses the optimization of different and often conflicting objectives leading to a multiple objective design *** this paper,a switching strategy is proposed to solve the multiple objective controller *** controller is designed based on a set of performance *** realization considerations are used to ensure overall closed loop *** matrix inequalities are employed in the controller design *** objective designs are always prone to over conservatism,which is greatly reduced by the switching *** results are used to show the effectiveness of the design methodology.

关键词： multi-objective control switching stability state space realization linear matrix inequalities(LMI)

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Fault-Tolerant Fuzzy control System Design with Multiple Indices Constraints: The Actuator Faults Case

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IFAC Proceedings Volumes 2009年第8期42卷 1061-1066页

作者： Xiaodong Han Dengfeng Zhang Dexiao Xie Zhiquan Wang School of Automation Nanjing University of Science and Technology Nanjing 210094 P. R. China Institute of Advanced Process Control Zhejiang University Hangzhou 310027 P. R. China

This paper is concerned with the design of fault tolerant controllers for a class of continuous-time nonlinear systems with multiple performance criteria constraints. The Takagi and Sugeno fuzzy model is employed to represent a nonlinear system. Based on the decomposition and equivalence principle, the state feedback controllers are designed to guarantee the fuzzy closed-loop system satisfying the pre-specified regional pole index, H ∞ norm-bound constraint on disturbance attenuation and having the quadratic cost performance simultaneously, in the actuator failure cases. Furthermore, the consistency of the performance indices mentioned earlier is set up for fault tolerant control. Finally, numerical simulations are given to illustrate the effectiveness of the proposed design methods.

关键词： fault tolerant system actuator fuzzy control performance indices nonlinear systems

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Identification, Prediction and Detection of the process Fault in a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

Identification, Prediction and Detection of the Process Faul...

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International Conference on Computer and Electrical Engineering, ICCEE

作者： Masoud Sadeghian Alireza Fatehi Department of Mechatronics Engineering Sharif University of Technology International Campus Kish Island Iran Advanced Process Automation and Control (APAC) Research group Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.

关键词： Fault diagnosis Fault detection Kilns Production facilities Electrical fault detection Fuzzy systems Delay estimation Nonlinear systems Predictive models automation

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Identification of Nonlinear Predictor and Simulator Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

Identification of Nonlinear Predictor and Simulator Models o...

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International Conference on Computer and Electrical Engineering, ICCEE

作者： Masoud Sadeghian Alireza Fatehi Department of Mechatronics Engineering International Campus Sharif University of Technology Kish Island Iran Advanced Process Automation and Control (APAC) Research group Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.

关键词： Predictive models Kilns Computational modeling Power system modeling Production facilities Nonlinear control systems Chemical technology Nonlinear equations Automatic control process control

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