Showing papers by "Mahdi Aliyari Shoorehdeli published in 2012"
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TL;DR: This study proposes a model-based robust fault detection and isolation (RFDI) method with hybrid structure that was tested on a single-shaft industrial gas turbine prototype model and has been evaluated based on the gas turbine data.
56 citations
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43 citations
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TL;DR: Simulation results show the effectiveness of these methods even with less rules and parameters in performance result and maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
Abstract: Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as "curse of dimensionality". The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
28 citations
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TL;DR: Fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated and the validity and feasibility of the proposed method for fault tolerant synchronization are demonstrated.
Abstract: In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization.
28 citations
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TL;DR: In this article, the authors proposed the chaotic control and the modified projective synchronization methods for unknown heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive backstepping control.
Abstract: This paper proposes the chaos control and the modified projective synchronization methods for unknown heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive backstepping control Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a regular or periodic motion beneficial for working with a particular condition As chaotic signals are usually broadband and noise-like, synchronized chaotic systems can be used as cipher generators for secure communication Obviously, the importance of obtaining these objectives is specified when the dynamics of gyroscope system are unknown In this paper, using the neural backstepping control technique, control laws are established which guarantees the chaos control and the modified projective synchronization of unknown chaotic gyroscope system In the neural backstepping control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions Also, the adaptation laws of the on-line estimators are derived in the sense of Lyapunov function Thus, the unknown chaotic gyroscope system can be guaranteed to be asymptotically stable Also, the control objectives have been achieved The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the Jacobian matrix, which makes it simple and convenient Also, it is a systematic procedure for modified projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not Notice that it needs only one controller to realize modified projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented It seems that the proposed method can be useful for practical applications of chaotic gyroscope systems in the future Numerical simulations are presented to verify the proposed control and synchronization methods
18 citations
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TL;DR: Experimental validation of proposed fuzzy logic controller for trajectory tracking and vibration control of a flexible joint manipulator reveals efficiency of proposed controller and the results are compared with LQR method.
Abstract: A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented.
18 citations
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TL;DR: In this paper, feature selection and classification methods based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI).
Abstract: Breast cancer Dynamic magnetic resonance imaging (MRI) has emerged as a powerful diagnostic tool for breast cancer detection due to its high sensitivity and has established a role where findings from conventional mammography techniques are equivocal[1]. In the clinical setting, the ANN has been widely applied in breast cancer diagnosis using a subjective impression of different features based on defined criteria. In this study, feature selection and classification methods based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) 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 was collected from 2004 to 2006. A forward selection method is applied to find the best features for classification. Moreover, several neural networks classifiers like MLP, PNN, GRNN and RBF has been presented on a total of 112 histopathologically verified breast lesions to classify into benign and malignant groups. Also support vector machine have been considered as classifiers. Training and recalling classifiers are obtained with considering four-fold cross validation.
13 citations
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TL;DR: In this article, the authors proposed the chaos control and generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control (GRAC).
Abstract: This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to verify the proposed control and synchronization methods. Finally, the effectiveness of the proposed methods is discussed.
12 citations
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TL;DR: In this article, the modified projective synchronization for heavy symmetric dissipative gyroscope systems via backstepping control is proposed to ensure the stability of the controlled closed-loop.
Abstract: This paper proposes the modified projective synchronization for heavy symmetric dissipative gyroscope systems via backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits complex and chaotic motions. Using the backstepping control technique, control laws are established which guarantees the hybrid projective synchronization including synchronization, anti-synchronization and projective synchronization. By Lyapunov stability theory, control laws are proposed to ensure the stability of the controlled closed-loop. Numerical simulations are presented to verify the proposed synchronization approach. This paper demonstrates that synchronization and anti-synchronization can coexist in dissipative gyroscope systems via nonlinear control.
10 citations
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TL;DR: It is demonstrated that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO.
Abstract: Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.
9 citations
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TL;DR: A distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system and the intelligent controls applied are emulated from the neural controls in biological system.
Abstract: Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. Till now, no considerable investigation of this subject is presented in the interdisciplinary sciences. The goal of this paper is to present a distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system. In this study, the basic knowledge of human eye movements control is explained to show how the neural networks in the brainstem control the human eye movements. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics. Intelligent methods such as artificial neural networks and fuzzy neural networks are proposed to control the human eye movements and numerical simulations are presented. It is discussed that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system.
01 Jan 2012
TL;DR: In this paper, a nonlinear control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic gyroscope synchronization is presented, which is based on Lyapunov stability theory and some generic sufficient conditions for global asymptotic control are attained.
Abstract: This paper presents a nonlinear control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic gyroscope synchronization. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. In this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is trying to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability and level of vibration reduction. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible joint manipulator.
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TL;DR: In this paper, a new combination of nonlinear backstepping scheme with fuzzy system is presented to control both position regulation and anti-swing control to avoid collision with other equipments.
Abstract: One of the common industrial structures that are used widely in many harbors and factories are overhead crane. Overhead cranes are usually operated manually or by some conventional control methods. In this study, a new combination of nonlinear backstepping scheme with fuzzy system is presented to control both position regulation and anti-swing control to avoid collision with other equipments. This study uses two different fuzzy systems: off-line and on-line to consider different concept. The backstepping design procedure consists of two steps. In all steps to design backstepping controller, Lyapunov stability theorem is used to stability analysis and control design problems. Furthermore, the stability of the overhead crane with nonlinear fuzzy backstepping controller is discussed. Simulation results illustrated the validity of the proposed control algorithm.
Key words: On-line fuzzy design, off-line fuzzy design, backstepping method, over head crane.
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TL;DR: Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE) solution proves that all operating zones of the WBF are of non-linear sub-systems.
Abstract: The walking beam furnace (WBF) is one of the most prominent process plants
often met in an alloy steel production factory and characterized by high
non-linearity, strong coupling, time delay, large time-constant and time
variation in its parameter set and structure. From another viewpoint, the
WBF is a distributed-parameter process in which the distribution of
temperature is not uniform. Hence, this process plant has complicated
non-linear dynamic equations that have not worked out yet. In this paper, we
propose one-step non-linear predictive model for a real WBF using non-linear
black-box sub-system identification based on locally linear neuro-fuzzy
(LLNF) model. Furthermore, a multi-step predictive model with a precise long
prediction horizon (i.e., ninety seconds ahead), developed with application
of the sequential one-step predictive models, is also presented for the
first time. The locally linear model tree (LOLIMOT) which is a progressive
tree-based algorithm trains these models. Comparing the performance of the
one-step LLNF predictive models with their associated models obtained
through least squares error (LSE) solution proves that all operating zones
of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy
Steel factory is utilized for identification and evaluation of the proposed
neuro-fuzzy predictive models of the WBF process.
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TL;DR: In this article, the authors presented energy reduction with anticontrol of chaos for nonholonomic mobile robot system, where the error of the robot system has been synchronized with chaotic gyroscope for reducing energy and increasing performance.
Abstract: This paper presents energy reduction with anticontrol of chaos for nonholonomic mobile robot system. Anticontrol of chaos is also called chaotification, meaning to chaotify an originally non-chaotic system, and in this paper error of mobile robot system has been synchronized with chaotic gyroscope for reducing energy and increasing performance. The benefits of chaos synchronization with mechanical systems have led us to an innovation in this paper. The main purpose is that the control system in the presence of chaos work with lower control cost and control effort has been reduced. For comparison of proposed method, the feedback linearization controller has also been designed for mobile robot with noise. Finally, the efficacies of the proposed method have been illustrated by simulations, energy of control signals has been calculated, and effect of Alpha (: a constant coefficient is used beside of chaotic system) variations on the energy of control signals has been checked.
01 Jan 2012
TL;DR: In this article, the authors proposed the chaos control and the modified projective synchronization methods for chaotic dissipative gyroscope systems using variable structure control and Lyapunov stability theory.
Abstract: This paper proposes the chaos control and the modified projective synchronization methods for chaotic dissipative gyroscope systems. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. Using the variable structure control technique, control laws are established which guarantees the chaos control and the modified projective synchronization. By Lyapunov stability theory, control lows are proposed to ensure the stability of the controlled and synchronized system. Numerical simulations are presented to verify the proposed control and the synchronization approach. This paper demonstrates that synchronization and anti-synchronization can coexist in dissipative gyroscope systems via variable structure control.
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TL;DR: In this paper, a fault tolerant control of inverted pendulum via online fuzzy backstepping and anti-control of chaos is presented to increase the fault tolerant capability of pendulum.
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TL;DR: This paper proposes the generalized projective synchronization for chaotic heavy symmetric gyroscope systems versus external disturbances via sliding rule-based fuzzy control based on Lyapunov stability theory and fuzzy rules and demonstrates the validity and feasibility of the proposed method.
Abstract: This paper proposes the generalized projective synchronization for chaotic heavy symmetric gyroscope systems versus external disturbances via sliding rule-based fuzzy control. Because of the nonlinear terms of the gyroscope, the system exhibits complex and chaotic motions. Based on Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are attained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the Eigen values of the Jacobian matrix. It is a systematic procedure for synchronization of chaotic systems. It can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. It needs only one controller to realize synchronization no matter how much dimensions the chaotic system contains, and the controller is easy to be implemented. The designed controller is robust versus model uncertainty and external disturbances. Numerical simulation results demonstrate the validity and feasibility of the proposed method.
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21 May 2012TL;DR: The design and optimization process of neuro fuzzy controller is based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS), and implementation of this haptic scissors control system had been realized under MATLAB/SIMULINK environment.
Abstract: This paper presents construction of haptic scissors that can be used for surgical training, using the MATLAB-Simulink Real-Time Workshop environment and SolidWorks software. In this instrument we have force feedback in every position and angle of the scissors. The data which was already gathered in [1,2] for different tissues were used here. However, the previous simulations lack flexibility because only the exact information obtained during data acquisition are used. In this paper the data has been made smoother by using Neuro Fuzzy Controller. Design of a neuro fuzzy controller is considered in this work because of its insensitivity to disturbances and uncertainties of model parameters. The design and optimization process of neuro fuzzy controller is based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS), and implementation of this haptic scissors control system had been realized under MATLAB/SIMULINK environment. The experimental results demonstrates the efficiency of this design procedure.
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TL;DR: In this article, the authors presented fault tolerant control of inverted pendulum via online fuzzy backstepping and anti-control of chaos, which is used frequently in robotic applications and can be found in different forms.
Abstract: This study presents fault tolerant control of inverted pendulum via on-line fuzzy backstepping and anti-control of chaos The inverted pendulum is used frequently in robotic applications and can be found in different forms Based on Lyapunov stability theory for backstepping design, the nonlinear controller and some generic sufficient conditions for asymptotic control are attained Also in this study, anti-control of chaos is applied to increase the fault tolerant of inverted pendulum To achieve this goal, the chaos dynamic must be created in the inverted pendulum system So, the inverted pendulum system has been synchronized to chaotic gyroscope system In this study, control and anti-control concepts are applied to achieve the high quality performance of inverted pendulum system The performances of the proposed control are examined in terms of fault tolerant capability Finally, the efficacies of the proposed methods are illustrated by simulations
Key words: Backstepping design, anti-control, chaos, synchronization, chaotic gyroscope, on-line fuzzy design, inverted pendulum, fault tolerant control
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15 May 2012TL;DR: In this paper, fault detection and isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath Geva (GGG) clustering is employed for the intent of fault isolation Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of principle component analysis and genetic algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity
Abstract: In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering is employed for the intent of fault isolation Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and Genetic Algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computation problems There are two main percentile criteria for validation of fault detection namely specificity and sensitivity Evaluation of fault isolation has been depicted in confusion matrix For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery