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Showing papers by "Mahdi Aliyari Shoorehdeli published in 2009"


Journal ArticleDOI
01 Mar 2009
TL;DR: It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints and stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.
Abstract: This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input-output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.

133 citations


Journal ArticleDOI
TL;DR: It is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model.

109 citations


Proceedings ArticleDOI
11 Jun 2009
TL;DR: In this paper feature selection is argued as an important problem via diagnosis and it is demonstrated that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates.
Abstract: Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore support vector machines and probabilistic neural network are proposed to classification. These methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper feature selection is argued as an important problem via diagnosis and demonstrate that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates. Thyroid disease datasets are taken from UCI machine learning dataset.

59 citations


Journal ArticleDOI
TL;DR: In this article, a hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm.
Abstract: This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.

39 citations


Journal ArticleDOI
TL;DR: The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system and it is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient.
Abstract: This paper proposes the generalized projective synchronization for chaotic systems via Gaussian Radial Basis Adaptive Backstepping Control. In the neural backstepping controller, a Gaussian radial basis function is utilized to on-line estimate the system dynamic function. The adaptation laws of the control system are derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. 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, which makes it simple and convenient. Also, it is a systematic procedure for generalized 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. Note that it needs only one controller to realize generalized projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The proposed method is applied to three chaotic systems: Genesio system, Rossler system, and Duffing system.

28 citations


Book ChapterDOI
01 Jan 2009
TL;DR: A modified discrete particle swarm optimization (PSO) is successfully used based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints.
Abstract: Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and Kennedy (Ebarhart, Kennedy, 1995; Kennedy, Eberhart, 1995; Ebarhart, Kennedy, 2001). The PSO is a population based search algorithm based on the simulation of the social behavior of birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the graceful and unpredictable choreography of a bird folk. Each individual within the swarm is represented by a vector in multidimensional search space. This vector has also one assigned vector which determines the next movement of the particle and is called the velocity vector. The PSO algorithm also determines how to update the velocity of a particle. Each particle updates its velocity based on current velocity and the best position it has explored so far; and also based on the global best position explored by swarm (Engelbrecht, 2005; Sadri, Ching, 2006; Engelbrecht, 2002). The PSO process then is iterated a fixed number of times or until a minimum error based on desired performance index is achieved. It has been shown that this simple model can deal with difficult optimization problems efficiently. The PSO was originally developed for realvalued spaces but many problems are, however, defined for discrete valued spaces where the domain of the variables is finite. Classical examples of such problems are: integer programming, scheduling and routing (Engelbrecht, 2005). In 1997, Kennedy and Eberhart introduced a discrete binary version of PSO for discrete optimization problems (Kennedy, Eberhart, 1997). In binary PSO, each particle represents its position in binary values which are 0 or 1. Each particle's value can then be changed (or better say mutate) from one to zero or vice versa. In binary PSO the velocity of a particle defined as the probability that a particle might change its state to one. This algorithm will be discussed in more detail in next sections. Upon introduction of this new algorithm, it was used in number of engineering applications. Using binary PSO, Wang and Xiang (Wang & Xiang, 2007) proposed a high quality splitting criterion for codebooks of tree-structured vector quantizers (TSVQ). Using binary PSO, they reduced the computation time too. Binary PSO is used to train the structure of a Bayesian network (Chen et al., 2007). A modified discrete particle swarm optimization (PSO) is successfully used based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints (Yare & Venayagamoorthy, 2007). Choosing optimum input subset for SVM (Zhang & Huo, 2005),

27 citations


01 Jan 2009
TL;DR: This method determines main Principal Components can be used to detect fault during the operation of industrial process by neural classifier and is applied to simulated data collected from the Tennessee Eastman chemical plant simulator.
Abstract:  Abstract— This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm .This method determines main Principle Components can be used to detect fault during the operation of industrial process by neural classifier. This technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman chemical.

21 citations


Proceedings ArticleDOI
14 Jul 2009
TL;DR: In this article, a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances, is proposed, which combines Linear Quadratic Regulation (LQR), Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control.
Abstract: This paper proposes a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances. In this scheme, Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic gyro systems through the application of a single control signal.

16 citations


Proceedings ArticleDOI
23 Mar 2009
TL;DR: This work argues that feature selection is an important issue in face and non-face classification and proposes a method to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face.
Abstract: Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification. Automatic feature subset selection distinguishes the proposed method from previous face classification approaches. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space, spanned by the eigenvectors of the covariance matrix of the training images (i.e., coefficients of the linear expansion).Then we consider Linear Discrimination Analysis (LDA) to achieve a comparison result between these two methods of dimension reduction. Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face. Finally, a Probabilistic Neural Network (PNN) is trained to perform face classification using the selected eigen-feature subset. Experimental results demonstrate a significant improvement in error rate reduction.

4 citations


Proceedings ArticleDOI
01 Aug 2009
TL;DR: A Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system in presence of flow nonlinearities and internal friction.
Abstract: In this paper a Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. The architecture and learning procedure ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. It is shown that both these controllers can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of nonlinear mathematical model of the system.

Proceedings ArticleDOI
01 Aug 2009
TL;DR: This paper suggests a novel approach for control of a flexible-link based on the feedback-error-learning (FEL) strategy by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator.
Abstract: This paper suggests a novel approach for control of a flexible-link based on the feedback-error-learning (FEL) strategy. A radial basis function neural network (RBFNN) is used as an adaptive controller to improve the performance of a lead compensator controller in FEL structure. This scheme is developed by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator which requires only a linear model of the system for designing lead compensators and RBFNN controllers. The final controller should allow the user to command a desired tip angle position. The controller should eliminate the link's vibrations while maintaining a desirable level of response. Finally, the control performance of the proposed control approach for tip position tracking of flexible-link manipulator is illustrated by simulation result.