Jin-Shiuh Taur

Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties

A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.

Adaptive Principal component EXtraction (APEX) and applications

The authors describe a neural network model (APEX) for multiple principal component extraction. All the synaptic weights of the model are trained with the normalized Hebbian learning rule. The network structure features a hierarchical set of lateral connections among the output units which serve the purpose of weight orthogonalization. This structure also allows the size of the model to grow or shrink without need for retraining the old units. The exponential convergence of the network is formally proved while there is significant performance improvement over previous methods. By establishing an important connection with the recursive least squares algorithm they have been able to provide the optimal size for the learning step-size parameter which leads to a significant improvement in the convergence speed. This is in contrast with previous neural PCA models which lack such numerical advantages. The APEX algorithm is also parallelizable allowing the concurrent extraction of multiple principal components. Furthermore, APEX is shown to be applicable to the constrained PCA problem where the signal variance is maximized under external orthogonality constraints. They then study various principal component analysis (PCA) applications that might benefit from the adaptive solution offered by APEX. In particular they discuss applications in spectral estimation, signal detection and image compression and filtering, while other application domains are also briefly outlined. >

Decision-based neural networks with signal/image classification applications

Supervised learning networks based on a decision-based formulation are explored. More specifically, a decision-based neural network (DBNN) is proposed, which combines the perceptron-like learning rule and hierarchical nonlinear network structure. The decision-based mutual training can be applied to both static and temporal pattern recognition problems. For static pattern recognition, two hierarchical structures are proposed: hidden-node and subcluster structures. The relationships between DBNNs and other models (linear perceptron, piecewise-linear perceptron, LVQ, and PNN) are discussed. As to temporal DBNNs, model-based discriminant functions may be chosen to compensate possible temporal variations, such as waveform warping and alignments. Typical examples include DTW distance, prediction error, or likelihood functions. For classification applications, DBNNs are very effective in computation time and performance. This is confirmed by simulations conducted for several applications, including texture classification, OCR, and ECG analysis.

A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification

Hyperspectral imaging fully portrays materials through numerous and contiguous spectral bands. It is a very useful technique in various fields, including astronomy, medicine, food safety, forensics, and target detection. However, hyperspectral images include redundant measurements, and most classification studies encountered the Hughes phenomenon. Finding a small subset of effective features to model the characteristics of classes represented in the data for classification is a critical preprocessing step required to render a classifier effective in hyperspectral image classification. In our previous work, an automatic method for selecting the radial basis function (RBF) parameter (i.e., σ) for a support vector machine (SVM) was proposed. A criterion that contains the between-class and within-class information was proposed to measure the separability of the feature space with respect to the RBF kernel. Thereafter, the optimal RBF kernel parameter was obtained by optimizing the criterion. This study proposes a kernel-based feature selection method with a criterion that is an integration of the previous work and the linear combination of features. In this new method, two properties can be achieved according to the magnitudes of the coefficients being calculated: the small subset of features and the ranking of features. Experimental results on both one simulated dataset and two hyperspectral images (the Indian Pine Site dataset and the Pavia University dataset) show that the proposed method improves the classification performance of the SVM.

A Novel Fuzzy-Sliding and Fuzzy-Integral-Sliding Controller for the Twin-Rotor Multi-Input–Multi-Output System

In this paper, a novel fuzzy-sliding and fuzzy-integral-sliding controller (FSFISC) is designed to position the yaw and pitch angles of a twin-rotor multi-input-multi-output system (TRMS). With the coupling effects, which are considered as the uncertainties, the highly coupled nonlinear TRMS is pseudodecomposed into a horizontal subsystem and a vertical subsystem (VS). The proposed FSFISC consists of a fuzzy-sliding controller and an FISC for the horizontal and the VSs, respectively. The reaching conditions and the stability of the TRMS with the proposed controller are guaranteed. Simulation results are included to indicate that TRMS with the presented FSFISC can greatly alleviate the chattering effect and remain robust to the external disturbances. In addition, the performance comparisons with the proportional-integral-differential (PID) approach using a modified real-value-type genetic algorithm are provided to show that the FSFISC has better performance in the aspects of error and control indexes.

Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system

In this paper, a helicopter-like twin rotor multi-input multi-output system (TRMS) is decoupled and is fuzzy Takagi-Sugeno modeled with the complex nonlinear functions simplified as the propositional combination of linear functions. The design procedures of the fuzzy Takagi-Sugeno model of TRMS are detailed. Based on the derived fuzzy Takagi-Sugeno model, parallel distributed fuzzy LQR controller are designed to control the positions of the pitch and yaw angles in TRMS. The stability of the TRMS system with the proposed fuzzy controllers is discussed. Moreover, simulation results are included to indicate the effectiveness of the presented parallel distributed fuzzy LQR controllers for the TRMS.

Robust fuzzy control for a plant with fuzzy linear model

A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the experts linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonovs Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism.

A fuzzy if-then approach to edge detection

An edge detection approach based on fuzzy if-then rules is presented. This method avoids the difficulties of selecting parameter values in most of the edge detectors when no information about the images is known in advance. Combining all the if-then rules generates a set of potential edge pixels. The membership value of being an edge point for each pixel is assigned by a membership function. The pseudocentroid of a set of potential edge points is used as the threshold for the decision of selecting a real set of edge pixels. Comparison studies with the gradient, Laplacian, and Laplacian of Gaussian edge detectors having fixed parameters are provided. The empirical results show that the edge detector based on fuzzy if-then rules is generally more applicable to a wider class of images ranging from clear to very vague images.<<ETX>>

Design of a Fuzzy Controller With Fuzzy Swing-Up and Parallel Distributed Pole Assignment Schemes for an Inverted Pendulum and Cart System

In this paper, the inverted pendulum and cart system is effectively approximated by a Takagi-Sugeno (T-S) fuzzy model in a small range of angle near its equilibrium state. According to the proposed (T-S) fuzzy model of the inverted pendulum and cart system, a fuzzy controller designed with the parallel distributed pole assignment scheme is adopted to position the pendulum and cart at the desired states. The nonlinear friction is also considered. Moreover, a fuzzy swing-up controller is developed to swing up the pendulum on a limited rail under the constraints of control actions. Further, the stability of the inverted pendulum and cart system with the fuzzy parallel distributed pole assignment controller is studied. Simulation results are included to indicate the effectiveness and robustness of the proposed fuzzy controller.

Simplified type-2 fuzzy sliding controller for wing rock system

Wing rock is a highly nonlinear phenomenon in which aircrafts with slender delta wings undergo limit cycle roll oscillations at high angles of attack. A simplified type-2 fuzzy sliding controller is designed for suppressing wing rock phenomena and tracking the desired trajectories. To reduce the computational complexity of a type-reducer, the end points of a type-reduced set are approximated by the outputs of two standard fuzzy sliding mechanisms in the proposed simplified type-2 fuzzy sliding controller. Furthermore, the sliding modes of the fuzzy sliding control system are guaranteed. Simulation results are included to show the effectiveness of the proposed simplified type-2 fuzzy sliding controller.