Optimal Fuzzy Rules 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.

Thermal response and performance evaluation of floor radiant heating system based on fuzzy logic control

The floor radiant heating system is widely used in various buildings due to its advantages such as high efficiency, comfort, and uniform heating. Generally, research on floor radiant heating systems focuses on energy saving, but the issue of slow thermal response during the heating process has not been solved. To solve the problem of slow thermal response and uneven indoor thermal environment in floor radiant heating systems, a fuzzy logic control theory based on optimal fuzzy rules was proposed. The floor radiant heating system based on fuzzy logic control was established by the TRNSYS-MATLAB integrated control platform. The study compared the thermal response and thermal comfort performance of PID control, fuzzy logic control, and optimized fuzzy control methods. The results showed that fuzzy logic control performed better than PID control in controlling the system's thermal performance and indoor thermal environment. Compared to PID control, the optimized fuzzy control reduced the thermal response time by 64.60%, and the total energy consumption of the water pump and heat pump decreased by 222 kWh and 329 kWh, respectively. The proposed fuzzy logic control demonstrates favorable thermal response characteristics and holds substantial potential for energy conservation within the increasingly prevalent floor radiant heating systems.

Double-level optimal fuzzy association rules prediction model for time series based on DTW-i[formula omitted] fuzzy C-means

Information granulation theory has been widely used in short-term time-series forecasting research and holds significant weight. However, the error accumulation due to the lack of granular accuracy, along with information redundancy or deficiency in predictions, significantly affects short-term prediction accuracy. To compensate for these shortcomings, this paper proposes a double-level optimal fuzzy association rules prediction model for short-term time-series forecasting, which can strengthen the performance of information granulation in prediction. Firstly, this paper proposes a concept of breakpoints, which can accurately segment complex linear trends in time series and thus obtain a granular time series with highly accurate linear fuzzy information granules (LFIGs). Secondly, a improved distance is proposed to more accurately reflect the similarity between LFIGs by addressing counter-intuitive problems in the original distance. Theoretical analysis shows that the improved distance can effectively reduce errors in granular calculation. Then, a granule-suited fuzzy c-means algorithm is proposed for clustering LFIGs. Finally, this paper proposes a double-level optimal fuzzy association rules prediction model, which establishes the optimal rules for each cluster and selects the optimal two rules for prediction by the contribution of the clusters. The experimental results show that the prediction method effectively avoids the problems of information redundancy and information deficiency, and increases forecast accuracy. The model’s exceptional performance is demonstrated through comparative analysis with existing models in experimental investigations.

Optimization of fuzzy rules using neural network to control mobile robot in non-structured environment

It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately.

Genetic algorithm-based adaptive weighted fuzzy logic control (awFLC) for traction power control

One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%.

A New Fuzzy Sliding Mode Control Method for Permanent Magnet Synchronous Motor Servo System Based on Optimization of Fuzzy Rules

Aiming at the problems of inherent chattering of traditional sliding mode control system of permanent magnet synchronous motor and further improving the control precision, a new fuzzy exponential reaching law sliding mode control method optimized by improved differential evolution (DE) algorithm (DEFSMC) is proposed in this article. A new hyperbolic tangent function with fuzzy boundary layer is proposed to instead of the traditional switching function, and the improved DE algorithm is used to optimize the fuzzy rules of parameters in sliding mode controller. Simulation and experimental results prove that the proposed strategy can effectively reduce the chattering and overshoot, and improve the response speed and robustness of the system. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Optimization of fuzzy rules in sports highlight extraction method based on a genetic algorithm

Optimization of fuzzy rules in sports highlight extraction method based on a genetic algorithm

Design Of Fuzzy Rule-Based System Using Genetic Algorithms

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

A Co-optimization PSO for Fuzzy Rule-Based Classifier Design Problem Based on Enlarged Hedge Algebras

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers.

Intelligent control of induction motor for photovoltaic water pumping system

This work aims to improve the performance of direct torque control (DTC) technique for induction motor based photovoltaic (PV) water pumping system (PVWPS). The innovative aspect of this work consists in introducing the adaptive fuzzy logic control and the fuzzy logic control techniques as alternative approaches to conventional DTC to control the PVWPS. To ensure a good operation of the PV array, a variable step size incremental conductance (VSS INC) is implemented. Simulation studies of the proposed topology based on intelligent approaches will be investigated using Matlab/Simulink under various operating conditions to validate the suitability of the proposed PVWPS. From the obtained results, the proposed control strategies appear to be very convenient for water pumping applications.Article HighlightsAn intelligent control based on the advanced techniques is proposed for PV water pumping system.An adaptive fuzzy logic PID approach and optimal fuzzy rules are proposed for better operation of PV system.The suggested PV water pumping system achieves better performance, in particular minimization of torque and flux ripples, reduction of torque overshoot and high dynamic response.

A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule

A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.

A New Fuzzy Knowledge-based Optimisation System for Management of Container Yard Operations

Managing the container yard operations can be challenging as a result of various uncertainties associated with storing and retrieving containers from the yard. These associated uncertainties occur because the arrival of a truck to pick up the container is random, so the departure time of the container is unknown. The problem investigated in this paper emerges when newly arrived containers of different sizes, types and weights require storage operation in the same yard where other containers have already been stored. This situation becomes more challenging when the time of departure of existing container is not known. This study develops a new Fuzzy Knowledge-Based optimisation system named ‘FKB_GA’ for optimal storage and retrieval of containers in a yard that contains long stay pre-existing containers. The containers’ duration of stay factor is considered along with two other factors such as the similarity (containers with same customer) and the quantity of containers per stack. A new Multi-Layered Genetic Algorithm module is proposed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. An industrial case study is used to demonstrate the applicability and practicability of the developed system.

Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties

The intelligent decision making of multi-energy management in a microgrid is a non-trivial task due to the intermittent and stochastic nature of highly penetrated renewable energy sources and demand. To address such a challenge, the energy management system often adopts the prediction based day-ahead energy scheduling and real-time energy dispatch to optimally coordinate the operation of dispatchable components, e.g., battery-based energy storage and thermal units. This paper presents an adaptive optimal fuzzy logic based energy management solution to develop appropriate day-ahead fuzzy rules for real-time energy dispatch adaptively in the presence of operational uncertainties. The solution determines the optimal fuzzy inference system (e.g., the membership function shape and the inference rules set) based on the predicted information over a certain period through a novel offline meta-heuristic optimization algorithm. The real-time energy dispatch based on the obtained optimal fuzzy logic rules can be further carried out to meet the various operational criteria, e.g., minimal power fluctuation and operational cost. The proposed solution is extensively evaluated through simulation experiments in comparison with two existing approaches: the online rule-based dispatch method and the meta-heuristic optimization-based offline scheduling method. The numerical results demonstrate the superior performance of the proposed energy management solution.

Reinforcement Learning-Based Backstepping Control for Container Cranes

A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed for the control of container cranes. In this control scheme, the modified backstepping controller can handle the underactuated system of a container crane. Moreover, the gain of the modified backstepping controller is tuned by the reinforcement fuzzy Q-learning mechanism that can automatically search the optimal fuzzy rules to achieve a decrease in the value of the Lyapunov function. The effectiveness of the applied control scheme was verified by a simulation in Matlab, and the performance was also compared with the conventional sliding mode controller aimed at container cranes. The simulation results indicated that the used control scheme could achieve satisfactory performance for step-signal tracking with an uncertain lope length.

Application of neural network algorithm in fault diagnosis of mechanical intelligence

In recent years, mechanical fault diagnosis technology at home and abroad has developed rapidly, and its application has spread to various industrial fields. Due to the complex structure of rotating machinery, the ambiguity and complexity of fault characteristics and causes are common, and it is difficult to carry out fault diagnosis. Although many researches have been carried out and some research results have been obtained, the overall diagnostic level is not very high. High, which is extremely inconsistent with the status quo that is widely used in production. Therefore, it is of great significance to carry out fault diagnosis research on rotating machinery. In this paper, this paper briefly introduces the research and application of intelligent technology in equipment fault diagnosis, and gives the superiority of fuzzy neural network technology application in equipment fault diagnosis, and expounds the basics of fuzzy theory and neural network technology. Based on the principle, the advantages and disadvantages of the two in fault diagnosis are analyzed, and the necessity of combining the two is explained. Based on the previous research on the combination of fuzzy theory and neural network, a new combination method is proposed, and a fuzzy neural network model suitable for fault diagnosis is established. A fuzzy inference method based on fuzzy network is constructed, which realizes the knowledge of information through the extraction, optimization and screening of fuzzy rules. At the same time, the fuzzy neural network learning weights are transformed into case-based reasoning-based diagnostic guidance operators, which play an important role in the rapid extraction of knowledge and improve the diagnostic accuracy. The experimental results show that compared with the commonly used neural network and fuzzy theory fault diagnosis methods, this method can make up for the shortcomings of fuzzy theory and neural network alone. It has higher diagnostic accuracy and has a good application prospect in the field of rotating machinery fault diagnosis.

A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

Research on UBI Auto Insurance Pricing Model Based on Adaptive SAPSO to Optimize the Fuzzy Controller

In order to accurately determine the auto insurance rate of UBI, this paper proposes to use fuzzy controller to calculate the rate and optimize it by using the simulated annealing particle swarm algorithm with Metropolis criterion. Firstly, a fuzzy controller is constructed by selecting monthly mileage and violation times to calculate the self-underwriting coefficient. In order to eliminate the subjectivity defect of fuzzy controller, the correlation function of independent underwriting coefficient and historical risk data is proposed as the fitness function of evaluating fuzzy rules, using adaptive simulated annealing particle swarm optimization algorithm is intelligent search, according to the fitness value of continual iteration and optimize the optimal fuzzy rules. Finally, the fuzzy controller is reconstructed with the optimal fuzzy rules to estimate the auto insurance rate accurately. The results show that the adaptive simulated annealing particle swarm optimization algorithm can effectively extract the driving behavior information and can calculate the more reasonable and accurate autonomous underwriting coefficient. The results are highly correlated with the number of historical accidents and have the ability and stability of risk quantification.

Control of brushless DC motor based on fuzzy rules optimized by genetic algorithm used in hybrid vehicle

To improve the control precision of the closed-loop speed regulation of the brushless DC motor (Brushless DC motor, BLDCM) used in hybrid vehicles, the paper established a fuzzy controller for closed loop control of speed, taking the speed difference and the conversion rate of the speed difference as the inputs, and the increment of the supply voltage as the output, and proposed a method of optimizing fuzzy control rules based on genetic algorithm. Established the mathematical model of BLDCM in MATLAB/Simulink, simulated the traditional PID controller, the fuzzy controller and the fuzzy controller optimized by genetic algorithm. Then the hardware-in-the-loop motor control system is constructed based on dSPACE. The simulation and experiment prove that the optimization of fuzzy rules by genetic algorithm is an effective method to improve the accuracy of speed control, and it has good tracking performance for frequently changing speed requirements in hybrid vehicle.

Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm

Due to its high-tech confrontation and entertainment, soccer robots have attracted a large number of researchers to participate in it. Robot obstacle avoidance is an active research branch in the field of intelligent robot technology, and is a comprehensive subject covering multiple disciplines. The traditional obstacle avoidance method of soccer robots has the disadvantages of poor local optimization ability and slow convergence speed, and it is easy to fall into local extreme points. Based on the active obstacle avoidance strategy of local sensors and processors, this paper studies the method of automatic extraction and optimization of fuzzy rules of fuzzy path planner by genetic algorithm. This fuzzy control method does not depend on accurate environmental information, and has a small amount of computation and learning ability. A fitness function for evaluating the validity of a rule is proposed. The simulation results show that the algorithm has good applicability.

An Adaptive Framework for Event Detection in Wireless Sensor Network

A wireless sensor network is the new generation technology that holds the capability and it has transformed to interact with the physical environment according to the user’s demands. The features of a sensor network play the major role in various application fields that fit in the broad category for tracking, monitoring, automation and detection and in that category WSN plays the predominant role in event monitoring. Event detection in sensor networks is the process of observing; evaluating an event based on multiple attributes and generates an alarm at the appropriate time. A framework was developed to detect the event in in-door environment. The proposed event detection model comprises of three phases. In the first phase the network id grouped and cluster head is determined and subsequently composite model was developed to determine the event and the final phase involves in the fuzzy rule optimization. A simulation setup has created and the experimental evaluation validates the event detection mechanism through various metrics such as cluster communication, event accuracy are measured and evaluated. The results are compared with the energy efficient HEED algorithm and connected dominating set based cluster MCDS-CCH algorithm further the composite event detection mechanism with optimization rule outperforms than the well-established J48 decision tree classification algorithm.