This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.

This study involves implementing an intelligent controller using the fuzzy control algorithm to minimize cold weld and splash in inverter AC spot welding. This study presents an experimental curve of a welding output current and the maximum value of the Instantaneous Heating Rate (IHRmax) using the contact diameter of an electrode as the parameter. It also presents the experimental curve of a welding output current and the slope (S) of the instantaneous dynamic resistance using the instantaneous contact area of an electrode as the parameter. To minimize cold weld and splash, this study proposes an intelligent controller that controls the optimum welding current in real time by estimating the contact diameter of an electrode and the contact area of the initial welding part.

The fuzzy controller based on cycle-to-cycle control with output value adjustment factors (OAF) was developed for restoring gait of paralyzed subjects by using functional electrical stimulation (FES). Results of maximum knee flexion and extension controls with neurologically intact subjects suggested that the OAFs would be effective in reaching the target within small number of cycles and in reducing the error after reaching the target. Oscillating responses between cycles were also suppressed. The fuzzy controller was expected to be examined to optimize the OAFs with more subjects including paralyzed patients for clinical application.

This paper proposes a novel service configuration approach that can realize dynamic critical Quality of Service (QoS) adaptation to ever-changing and resource-limited ubiquitous computing environments. In the approach, service configuration is reduced to a Fuzzy Control System (FCS) which aims to achieve critical QoS variations on minimal level with less power cost. Two configuration strategies, service chain reconfiguration and QoS parameters adjustment, along with a configuration algorithm, are implemented to handle different types of QoS variations. A self-optimizing algorithm is designed to enhance the adaptation of the FCS. Simulation results validate the proposed approach.

In this study, a performance isolation mechanism based on a fuzzy control technique is developed in such a way that ambiguous situations caused when estimating the workload of cluster-based web servers, client request rates, and dynamic request rates can be represented effectively. The proposed mechanism involving a fuzzy-based technique is compared with a non-fuzzy technique in terms of the response time in the 95th percentile. Experiments showed that the proposed technique improves the performance of web servers that provide differentiated services.

The optimization of nonlinear feedback fuzzy system using the product-sum-gravity method is described in this paper. The fuzzy control discussed here is the nonlinear feedback control in which the feedback laws are determined by IF-THEN type fuzzy production rules through product-sum-gravity method. To prove existence of optimal control, we applied compactness of a set of membership functions in L2 space and continuity of the approximate reasoning, and prepared some propositions concerning product-sum-gravity method. By considering fuzzy optimal control problems as problems of finding the minimum (maximum) value of the integral cost (benefit) function on an appropriate set of membership functions, the existence of fuzzy optimal control is shown.

Functional Electrical Stimulation (FES) can be effective in assisting or restoring paralyzed motor functions. The purpose of this study is to examine experimentally the fuzzy controller based on cycle-to-cycle control for FES-induced gait. A basic experimental test was performed on controlling maximum knee extension angle with normal subjects. In most of control trials, the joint angle was controlled well compensating changes in muscle responses to electrical stimulation. The results show that the fuzzy controller would be practical in clinical applications of gait control by FES. An automatic parameter tuning would be required practically for quick responses in reaching the target and in compensating the change in muscle responses without causing oscillating responses.

Gains of a roll force AGC (Automatic Gain Controller) have been calculated at the first locked-on-time by FSU (Finishing-mill Set-Up model) in hot strip mills and usually these values are not adjusted during the operating time. Consequently, this conventional scheme cannot cope with the continuous variation of system parameters and circumstance, though the gains can be changed manually with the aid of experts to prevent a serious situation such as inferior mass production. Hence, partially uncontrolled variation still remains on delivery thickness. This paper discusses an effective online algorithm which can adjust the gains of the existing control system by considering the effect of time varying variables. This algorithm improves the performance of the system without additional cost and guarantees the stability of the conventional system. Specifically, this paper reveals the major factors that cause the variation of strip and explores the relationship between AGC gains and the effects of those factors through the analysis of thickness signal which occupy different frequency bands. The proposed tuning algorithm is based on the above relationship and realized through ANFIS (Adaptive-Neuro-based Fuzzy Interface System) which is a very useful method because its fuzzy logics reflect the experiences of professionals about the uncertainty and the nonlinearity of the system. The effectiveness of the algorithm is shown by several simulations which are carried out by using the field data of POSCO corporation (South Korea).

This paper presents a new Fuzzy Logic Controller (FLC) having the ability to support rational-powered membership functions. These functions are extended forms of triangular/trapezoidal membership functions, and also those functions which are generated by applying linguistic hedges. A two-input, single-output, nine-rule Takagi-Sugeno-Kang (TSK) type FLC is designed in 0.35 µm standard CMOS technology. This controller can also be used as a standard (Mamdani) type FLC having singleton output membership functions, as well as a Linguistic Hedge FLC (LHFLC). Mixed analog/digital realization of the circuit makes the design programmable and extendable, while having relatively low power consumption. Current mode realization of the circuits leads to simple and intuitive configurations. For a particular set of programming parameters, simulation results of the controller using HSPICE simulator and level 49 parameters (BSIM3v3), show an average power consumption of 5 mW, and an RMS error of 1.32% compared to ideal results obtained from MATLAB software.

The goal of this study was to design a practical fuzzy controller of the cycle-to-cycle control for multi-joint movements of swing phase of functional electrical stimulation (FES) induced gait. First, we designed three fuzzy controllers (a fixed fuzzy controller, a fuzzy controller with parameter adjustment based on the gradient descent method, and a fuzzy controller with parameter adjustment based on a fuzzy model) and two PID controllers (a fixed PID and an adaptive PID controllers) for controlling two-joint (knee and ankle) movements. Control capabilities of the designed controllers were tested in automatic generation of stimulation burst duration and in compensation of muscle fatigue through computer simulations using a musculo-skeletal model. The fuzzy controllers showed better responses than the PID controllers in the both control capabilities. The parameter adjustment based on the fuzzy model was shown to be effective when oscillating response was caused due to the inter-subject variability. Based on these results, we designed the fuzzy controller with the parameter adjustment realized using the fuzzy model for controlling three-joint (hip, knee, and ankle) movements. The controlled gait pattern obtained by computer simulation was not significantly different from the normal gait pattern and it could be qualitatively accepted in clinical FES gait control. The fuzzy controller designed for the cycle-to-cycle control for multi-joint movements during the swing phase of the FES gait was expected to be examined clinically.

We proposed a fuzzy control scheme to implement the cycle-to-cycle control for restoring swing phase of gait using functional electrical stimulation (FES). We designed two fuzzy controllers for the biceps femoris short head (BFS) and the vastus muscles to control flexion and extension of the knee joint during the swing phase. Control capabilities of the designed fuzzy controllers were tested and compared to proportional-integral-derivative (PID) and adaptive PID controllers in automatic generation of stimulation burst duration and compensation of muscle fatigue through computer simulations using a musculo-skeletal model. Parameter adaptations in the adaptive PID controllers did not significantly improve the control performance of the PID controllers. The fuzzy controllers were superior to the PID and adaptive PID controllers under several subject conditions and different fatigue levels. These results showed the fuzzy controller would be suitable to implement the cycle-to-cycle control of FES-induced gait.

This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the auto-tuning method of learning rate and momentum is proposed and applied to learning between middle layer and output layer, which arbitrates learning rate and momentum dynamically by using the fuzzy control system for the arbitration of the connected weight between middle layer and output layer. The experiment for the classification of number patterns extracted from the citizen registration card shows that compared with conventional networks such as delta-bar-delta algorithm and the ART2-based RBF network, the proposed method achieves the improvement of performance in terms of learning speed and convergence.

In this paper, a modified adaptive fuzzy sliding mode controller for a certain class of uncertain nonlinear systems is presented. We incorporate the fuzzy sliding mode control technique with a modified adaptive fuzzy control technique to design a modified adaptive fuzzy sliding mode controller so that the proposed controller is robust against the unmodeled dynamics and the approximation errors. Firstly, we establish a fuzzy model to describe the dynamic characteristics of the given uncertain nonlinear system. Then, based on the fuzzy model, a fuzzy sliding mode controller is designed. By considering both the information of tracking error and modeling error, the modified adaptive laws for tuning the adjustable parameters of the fuzzy model are derived based on the Lyapunov synthesis approach. Since the modified adaptive laws contain both the tracking error and the modeling error, it implies that the fuzzy model parameters would continuously converge until both the tracking error and modeling error converges to zero. An inverted pendulum control system is simulated to demonstrate the control performance by using the proposed method.

This paper presents two new intelligent methods to linearize the Multi-Carrier Power Amplifiers (MCPA). One of the them is based on the Neuro-Fuzzy controller while the other uses two small neural networks as a polar predistorter. Neuro-Fuzzy controllers are not model based, and hence, have ability to control the nonlinear systems with undetermined parameters. Both methods are adaptive, low complex, and can be implemented in base-band part of the communication systems. The performance of the linearizers is obtained via simulation. The simulation is performed for three different scenarios; namely, a multi-carrier amplifier for GSM with four channels, a CDMA amplifier and a multi-carrier amplifier with two tones. The simulation results show that Neuro-Fuzzy Controller (NFC) and Neural Network Polar Predistorter (NNPP) have higher efficiencies so that reduce IMD3 by more than 42 and 32 dB, respectively. The practical implementation aspects of these methods are also discussed in this paper.

This study proposes a novel adaptive fuzzy control methodology to remove disadvantages of traditional fuzzy approximation based control. Meanwhile, the highly uncertain robot manipulator is taken as an application with either guaranteed robust tracking performances or asymptotic stability in a global sense. First, the design concept, namely, feedforward fuzzy approximation based control, is introduced for a simple uncertain system. Here the desired commands are utilized as the inputs of the Takagi-Sugeno (T-S) fuzzy system to closely compensate the unknown feedforward term required during steady state. Different to traditional works, the assumption on bounded fuzzy approximation error is not needed, while this scheme allows easier implementation architecture. Next, the concept is extended to controlling manipulators and achieves global robust tracking performances. Note that a linear matrix inequality (LMI) technique is applied and provides an easier gain design. Finally, numerical simulations are carried out on a two-link robot to illustrate the expected performances.

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control (CMFC).

The conventional method of fuzzy control realizes only nonlinear PI (proportional and integral) control actions and does not have the D (derivative) control action required to effectively improve control performance. Hence, the improvement of control performance is limited. Therefore, in this paper, a method for simple improvement of the PI fuzzy control used conventionally is proposed. The method proposed here improves the control performance simply by combining, in parallel, the conventional PI fuzzy controller with the D control action which is realized by using the fuzzy inference method. Then, based on the simulation results for the first- and second-order lag systems with dead time, the effectiveness of the proposed fuzzy control is shown compared with the conventional PI fuzzy control.

This paper presents a new implementation of fuzzy filters for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The parameters of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.

In this paper, an extention for Haddad's method, which is the time-domain stability analysis on scalar nonlinear control systems, to multi-variable nonlinear control systems are proposed, and it is shown that these results are useful for the stability analysis of nonlinear control systems with various types of fuzzy controllers.

For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.