Supervised Learning of Fuzzy Logic Systems

In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia is developed. The proposed system uses a hierarchical fuzzy logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases that are used for prediction of interest rates in Australia.A hierarchical fuzzy logic system is developed to model and predict three‐month (quarterly) interest rate fluctuations. The system is further trained to model and predict interest rates for six‐month and one‐year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to model and predict interest rates.A novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back‐propagation hierarchical neural network system is also developed to predict interest rates for three‐month, six‐month and one‐year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.

This paper focuses on problems related to learning rules using numerical data for the Hierarchical Fuzzy Logic Systems (HFLS) described in [12]. Using hierarchical structure of Fuzzy Logic Systems (FLS) complex problems could be divided into subproblems with smaller dimensions. “Hierarchical” means that fuzzy sets produced as output of one of fuzzy logic systems are then processed as an input of another one as the sets of auxiliary variables. The main problem is to learn a rulebase with numerical data, which does not contain any data for those auxiliary variables. Learning rules for FLS in short could be accomplished by using many different approaches, building one, complex rulebase using all available input and output variables for complex problems. Our learning method based on the Wang & Mendel (W&M) method adopted for the HFLS with selective activation of unit FLS were introduced in [13]. The main scope of this paper is to extend our method applying quality measures of IF-THEN rules in the sense of Wu & Mendel (Wu&M) to remove conflicting rules. The proposal presented in this paper operates on a type-1 HFLS, built with the fuzzy logic systems (in the sense of Mamdani). An example of single-player games, i.e. where the “enemy” is controlled by agents is used. Two new problems are briefly introduced.

The application of hierarchical fuzzy logic system using genetic fuzzy rule generator architecture to robot navigation in presence of moving obstacles is considered. A hierarchical fuzzy logic system is proposed for collision avoidance and guidance of a robot systems. Simulation results and analysis are presented to show the effectiveness of the proposed method.

This paper concentrates on hierarchical fuzzy logic using the singular perturbation approach for flexible-link robot arm control. A two-time-scale fuzzy logic controller is applied to such a system. A large-scale system is decomposed into a finite number of reduced-order subsystems, thereby eliminating the need for a large-sized inference engine. In this paper, a fast-subsystem controller damps out the vibration of the flexible structure by means of two hierarchical fuzzy logic controllers. Another, slow-subsystem fuzzy controller dominates the trajectory tracking. It appears that the fuzzy control method is quite useful as regards reliability and robustness.

This paper presents a fuzzy logic control strategy on four-wheel steering(4WS) vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model based on ADAMS can accurately predict the dynamics performance of the vehicle. Fuzzy logic is applied to track the yaw velocity of the two degrees of freedom ideal model through the co-simulation of ADAMS and Matlab Fuzzy control unit with the optimized membership function. The fuzzy control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). Single lane change test is chosen to validate the fuzzy control logic strategy. Simulation result shows that four-wheel steering vehicle with the fuzzy control logic strategy can improve vehicle handling stability greatly comparing with traditional front wheel steering.

Predictive worker safety assessment through on-site correspondence using multi-layer fuzzy logic in outdoor construction environments

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

This paper considers the use of a multi-agent system for control of a multi traffic lights. The proposed model consists of a community of agents that autonomously act in cooperation with other agents using a set of fuzzy logic (FL) systems to control traffic at several intersections. Each agent system acts initially independently to control each traffic signal in an online real-time manner. Each agent adjusts traffic timing on each traffic signal. The effectiveness of the multi-agent system for controlling traffic signals is established through simulations. The multi-agent system used controls multi traffic signals. The results are promising, and they support the expandability of the model used using hierarchical fuzzy logic systems and multi-agent systems.

This paper presents a method of constructing accurate nonlinear dynamic MIMO models using a class of fuzzy logic systems (FLSs). Generally, the design of FLS models involves identification of both the FLS structure (linguistic fuzzy IF-THEN rules) and parameter tuning of fuzzy membership functions. Most current techniques treat these parts separately, which may result in a suboptimal solution. The authors propose to optimize the two parts simultaneously using a genetic algorithm (GA), a stochastic global search method which explores the solution space in manner similar to natural evolution. In the authors' work new FLS domain specific operators are introduced that significantly reduce search time, and at the same time, increase the accuracy of results. The FLS model obtained from GA search is further fine-tuned using a conjugate gradient method. To illustrate the proposed method, the authors show that the FLS models compare well to other nonparametric modeling techniques, such as standard feedforward neural networks on an example of a nonlinear system. In addition, a FLS offers qualitative model description in terms of the linguistic fuzzy rules which may contribute to the understanding and control of the process. These merits make the FLS models attractive for use in model predictive control and other process applications.

This paper developed a fuzzy logic controller with a genetic algorithm control system for sensor based pipeline robot navigation in pipe environment. The fuzzy controller creates initial membership functions and fuzzy logic rules. the genetic algorithm, which automatically designs a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the pipeline robot, optimizes the fuzzy controller. This GA-fuzzy system is used to control the mobile pipeline robot, which is equipped with an array of ultrasonic sensors to acquire the distance between the pipeline robot and obstacle. On the basis of this system, a strategy for combining local behavior control with global behavior control planning was proposed in this paper. Finally a pipeline robot runs in simulation environment. The experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the pipeline robot in different pipe environment.

Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Fuzzy rule base systems are expert systems rely on fuzzy set theory. Here the knowledge of human expert is transfered to the artificial model via fuzzy rules. Therefore, preciseness, completeness and coverage of fuzzy rules in a fuzzy system is vital for the accuracy and plausibility of fuzzy reasoning. However, in such cases where the human expert is unable to supply the rules sufficiently, data-based automatic rule generation methods attract attention. In this study, 2 linear and 2 evolutionary approaches of automatic fuzzy rule generation methods are investigated. The investigated linear solutions contain Wang-Mendel Method and E2E-HFS, while MOGUL and IVTURS-FARC are the selected evolutionary approaches. Wang-Mendel and MOGUL is commonly considered as basic methods of the group they belong to. IVTURS-FARC is distinguished with its ability to handle interval valued fuzzy sets. Among the rest of the algorithms, E2E-HFS is unique with its weak dependency to data. Because it only use some simple properties of corresponding input variable. In order to compare the completeness and the accuracy of automatically generated fuzzy rules, several experiments are performed on different software defect prediction datasets, and the classification performance of resulting fuzzy systems is evaluated. Provided results show that even if training of evolutionary approaches seem to be more precise, similar accuracy can be achieved by linear approaches, and they perform better regarding the experiments on unseen data.

Designing fuzzy logic system is one of the most popular and research-demanding NP-hard problems. It involves numerous parameters like shape and location of fuzzy sets, antecedents and consequents of fuzzy rule base and other strategic parameters like aggregation, implication and defuzzification methods. Time series forecasting has also become increasingly popular for the applications like share market prediction, weather forecasting. Many researchers have investigated the use of fuzzy logic system for forecasting of time series. In this paper, the authors have investigated the design framework of fuzzy logic systems for forecasting benchmark Mackey–Glass time series. Designing fuzzy logic systems is a class of NP-hard problems which is evolved using most popular and recent evolutionary algorithms. Authors have evolved fuzzy logic system using genetic algorithm, particle swarm optimization, artificial bee colony optimization, firefly algorithm and whale optimization algorithm. Finally, from simulations, it is found that whale optimization algorithm requires less time and shows fuzzy system predictions are more precise than others.

In this paper, the eight kinds of on-line adaptive, learning and evolutionary strategies for fuzzy logic control are systematically introduced. All these afore-mentioned strategies have some drawbacks in terms of generalization and formulation. Hence a systematic way of combination and hybridization of these strategies will be very useful for improving the learning capacity and performance of algorithms based on these strategies. It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms or other algorithms should be able to compensate for the disadvantages of neural networks learning.