Fuzzy Logic-based Learning Research Articles

Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz–Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans.

Field programmable gate array implementation of fuzzy variable step size adaptive linear element for adaptive frequency estimation

Accurate estimation of power signal frequency is an important requirement for many application areas that include system protection, energy quality monitoring and instrumentation. Though significant efforts have been made since long to develop potent algorithms for accurate estimation of power signal frequency, still their accuracy and convergence speed are a challenge under sudden frequency drift and variations. Therefore, this study focuses on a low complexity adaptive linear element filter using quadratic signal model, whose parameters are adjusted using a fast variable step size fuzzy logic-based learning algorithm to provide better convergence and noise rejection properties for the estimation of frequency from noisy and distorted signals. In addition, the new filter has also been implemented on a field programmable gate array hardware and Xilinx 14.2 with Sysgen software for the tracking of dynamic signal parameters in single and three phase power networks. Various numerical and experimental results are addressed for estimation of frequency of time varying sinusoids.

Less computationally intensive fuzzy logic (type-1)-based controller for humanoid push recovery

In this study, we present a new type-1 fuzzy logic-based controller for push recovery by humanoids. The objective of this study is to develop an intelligent controller and to implement biologically inspired push recovery for humanoid robots. The fuzzy inference system takes two crisp values as inputs, which are fuzzified, before a number of rules are applied, and finally the output is defuzzified to convert it into a crisp value. We apply fuzzy rules to our model, which we simulate in an unstructured environment. The objective is to reduce the fuzzy rules and make the fuzzy inference set less computationally intensive and fast, as well as exploiting the advantages of easy trainability and high generalizability. The fuzzy logic-based controller can predict the requisite push recovery strategy and whether the robot will be able to recover or fall. The architecture has a hierarchical design. The first fuzzy inference system (FIS1) is based on two input variables: the force and the direction of motion, where the result depends on the magnitude of the force applied to the body and direction in which the body moves after being pushed. FIS1 can determine small, medium, and large forces in term of roll and pitch effects on the body. These outputs are the input variables employed by FIS2 to predict the push recovery strategy that will be applied, and eventually the robot will be able to recover from a push or fall. The term “auto-leaning” from human autonomy is introduced in the context of push recovery. We extend the Gordon model for balancing humanoids using fuzzy logic by considering the effects of roll, pitch, and yaw. This extends earlier research in the field of humanoid push recovery, where the force was only applied in one direction, to studies of pushing in different directions. In a cluttered environment, pushing is a very common experience, from which humans can readily recover whereas humanoid robots cannot. Humanoid robots have more degrees of freedom, thus finding solutions using alternative methods is not a simple task. The novel feature of this study is the introduction of an intuitive fuzzy logic-based learning approach and we show that it is fast and effective. This exhaustive fuzzy logic-based design is the main focus of this study.

Effective learning system techniques for human–robot interaction in service environment

HRI (Human–Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.

Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery

The objective of this work is to develop a state-of-charge (SOC) estimation system for the lead-acid battery, which is free from the time-dependent variation of the battery characteristics. In this system, the SOC is estimated by an improved Coulomb metric method, and the time-dependent variation is compensated by using a learning system. The learning system tunes the Coulomb metric method in such a way that the estimation process remains error free from the time-dependent variation. The proposed learning system uses the fuzzy logic, which is not used for estimation of SOC but perform as a component of learning system. The fuzzy logic is used as a soft computing device for a multi-variables function evolution. During learning process the system automatically generates a new fuzzy rule base, and replaces the old fuzzy rule base. Results of the simulations as well as the experiments on an 8-bit microcontroller are also included which indicate the effectiveness of the proposed method.

A transformer resonant phase‐shifted PWM DC–DC converter for X‐ray power generator and its fuzzy logic‐based learning control scheme

AbstractIn general, medical‐use high‐voltage X‐ray power generators are operated at a variety of required output DC voltages and output current settings for the X‐ray tube. A phase‐shifted PWM inverter‐fed DC–DC power converter with a high‐voltage transformer parasitic resonant link which is used for an X‐ray power generator inherently has stiff nonlinear characteristics due to phase‐shifted voltage regulation and diode cutoff operation in a high‐voltage rectifier because of the wide load setting ranges in practical applications. However, the superior output voltage response required for a medical‐use X‐ray power generator could not be sufficiently implemented by various modern linear control approaches because a precise mathematical description with dynamic modeling could not be formulated for wide load variations. For controlling such a nonlinear DC–DC power conversion system, a fuzzy reasoning‐based learning control technique that duplicates human capabilities and much experience in understanding the dynamic and static behavior of the system are more suitable and effective. This paper presents a phase‐shifted PWM full bridge inverter‐type high‐voltage DC–DC power converter with a high‐voltage transformer parasitic link and its fuzzy‐based learning controller, which has excellent dynamic and static behavior, for use in a medical X‐ray power generator operating at a wide variety of load settings. The effectiveness of the feasible fuzzy learning technique used for control of the DC–DC power converter is discussed and evaluated based on computer simulations and experimental results in a laboratory setup. It is demonstrated that the feasible fuzzy learning control scheme suitable for this converter is more effective and practical in improving the system output voltage response performance in both transient and steady states for medical‐use X‐ray power generators. The results of simulations and experiments for this converter are presented in order to illustrate the operating performance of the proposed X‐ray power generator operated by the feasible fuzzy‐based learning control implementation. © 2001 Scripta Technica, Electr Eng Jpn, 136(2): 58–71, 2001

Robust self-learning fuzzy controller design for a class of nonlinear MIMO systems

A new learning paradigm that can be applied for the design of a fuzzy logic-based learning controller that is robust to external signals such as disturbances and set-point changes is proposed in the paper. It is well known that the self-organizing fuzzy controller proposed by Procyk and Mamdani is sensitive to external signals. Such a phenomenon may be observed in other types of direct fuzzy logic-based learning controllers that utilize an adaptation scheme in which the locational information of the current error state vector determines the degree of adaptation that should be made in the learning controller. To resolve the problem of sensitivity to external signals, it is proposed that learning and modification of the controller be made in consideration of the motional trend of the error state vector as well as its locational information. This paradigm is adopted in the design of a new robust self-learning fuzzy controller for a class of nonlinear MIMO systems. The well-known techniques of sliding mode control and the fuzzy decision making method are utilized to implement the proposed learning scheme in the fuzzy learning controller. Via simulation study and experimental results, the proposed learning controller is verified to be robust in the presence of external disturbances.

Transformer Resonant Phase-Shifted PWM DC-DC Converter for X Ray Power Generator and Its Fuzzy Logic-based Learning Control Scheme

In general, medical-use high-voltage X ray power generator works under a variety of operating conditions of the required output DC voltage and the output current setting values for the X ray tube. A phase-shifted PWM inverter-fed DC-DC power converter with a high-voltage transformer parasitic resonant link which is used for an X ray power generator inherently includes its stiff nonlinear characteristics due to phase-shifted voltage regulation and diode cut off operation in a high-voltage rectifier because of the wide load setting regulation ranges in practical applications. However, superior output voltage response performances of a quick rising time in a transient state and low ripple factor in a steady-state which have to be essentially required for a medical-use X ray power generator could not be sufficiently implemented by various linear modern control approaches because a precise mathematical description with dynamic modeling could not be formulated for wide load variations. For controlling such a nonlinear DC-DC power conversion system, a fuzzy-reasoning-based learning control technique to realize the human capability and much experience to understand the system dynamic and static behavior is more suitable and effective. This paper presents a phase-shifted PWM full bridge inverter type high-voltage DC-DC power converter with a high-voltage transformer parasitic resonant link and its fuzzy-based learning controller, which has excellent dynamic and static behaviors to be utilized in a medical-use X ray power generator operating under conditions of a variety of wide load settings. The effectiveness of feasible fuzzy learning control technique of the DC-DC power converter is discussed and evaluated on the basis of the computer simulation and experimental results in a laboratory set-up. It is proved that the feasible fuzzy-based learning control scheme suitable for this converter is more effective and practical to improve the system output voltage response performances in both the transient and steady states for medical-use X ray power generator. The simulated and experimental results of this converter are actually illustrated in order to substantiate the operating performance validity of the inverter type medical-use X ray power generator operated by the feasible fuzzy-based learning control implementation.