FEWER HYPER-ELLIPSOIDS FUZZY RULES GENERATION USING EVOLUTIONAL LEARNING SCHEME

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

The central point in designing a fuzzy system for modeling and control is the generation of a set of efficient and effective fuzzy rules. Generally, fuzzy rules can be generated either from heuristics or from experimental data. Heuristic fuzzy rule generation, as discussed in Chapter 1, consists of the following main steps: Determination of the structure of the fuzzy systems. This includes mainly the selection of the inputs and outputs. Usually, a multi-input multi-output fuzzy systems can be decomposed into a number of multi-input single-output fuzzy systems. Definition of linguistic terms and their fuzzy membership function for each fuzzy variables. If a Mamdani fuzzy system is designed, each input and output can be seen as a linguistic variable. Therefore, it is necessary to define the linguistic terms (e.g., Small, Medium, Large etc.) and their fuzzy membership function. Details about the definition of a linguistic variable is presented in Section 1.2.1. Determination of fuzzy inference method. For example, for fuzzy mapping rules, the fuzzy Cartesian product can be used to define the fuzzy relation. Refer to Section 1.2.2. Defuzzification. If the Mamdani type fuzzy rules are used, the fuzzy output derived from the fuzzy rule base should be defuzzified. Refer also to Section 1.2.2 for different defuzzification methods. If the Takagi-Sugeno fuzzy rules are used, the defuzzification is included in the fuzzy inference and a crisp output is obtained directly.

Conventionally modelling and simulation of complex nonlinear systems has been to construct a mathematical model and examine the system’s evolution or its control. This kind of approach can fail for many of the very large non-linear and complex systems being currently studied. With the invention of new advanced high-speed computers and the application of artificial intelligence paradigms new techniques have become available. Particularly neural networks and fuzzy logic for nonlinear modelling and genetic algorithms [Goldberg, D. (1989)] and evolutionary algorithms for optimisation methods have created new opportunities to solve complex systems [Bai, Y., Zhuang H. and Wang, D. (2006)]. This paper considers issues in design of multi-layer and hierarchical fuzzy logic systems. It proposes a decomposition technique for complex systems into hierarchical and multi-layered fuzzy logic sub-systems. The learning of fuzzy rules and internal parameters in a supervised manner is performed using genetic algorithms. The decomposition of complex nonlinear systems into hierarchical and multi-layered fuzzy logic sub-systems reduces greatly the number of fuzzy rules to be defined and improves the learning speed for such systems. In this paper a method for combining subsystems to create a hierarchical and multilayer fuzzy logic system is also described. Application areas considered are - the prediction of interest rate, unemployment rate predication and electricity usage prediction. Genetic Algorithms can be used as a tool for design and generation of fuzzy rules for a fuzzy logic system. This automatic design and generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques namely, supervised and unsupervised. In supervised learning there are two distinct phases to the operation. In the first phase each individual is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as the fitness for the individual in the population of genetic algorithms. Supervised learning has successfully applied to solve some difficult problems. In this paper design and development of a genetic algorithm based supervised learning for fuzzy models with application to several problems is considered. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules that can be used in a fuzzy logic system for modelling, control and prediction

Fuzzy control strategy is developed for the dual-clutch single-axis torque coupling parallel hybrid electric vehicle. In this paper the torque distribution fuzzy controller which has been designed for the hybrid vehicle which is optimized by genetic algorithms. The simulation model of the hybrid vehicle was built upon matlab / simulink and ADVISOR software. Then a fuzzy rules and correspondent membership functions had been established and the input language variable and output language variable use trapeziform and deltoid membership functions. After design of fuzzy logic torque controller, the genetic algorithm was introduced and used it to optimize the fuzzy logic torque controller. Under typical condition NEDC, the fuzzy control strategy is optimized both by genetic algorithms with the constraint condition of economy performance and by integrated constraint conditions of economy performance and emission performance. Optimization results show that when the controller is only optimize fuzzy control strategy for economy performance the fuel consumption decreased by 5.3% but the emission of CO and NOx both increased, but when the controller both optimize fuzzy control strategy for economy performance and emission performance the fuel consumption decreased by 4.3% with emission quality improved. So the fuzzy control strategy optimize by the genetic algorithm can improve the fuel consumption obvious.

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.

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.

We examine the performance of compact fuzzy rule-based classification systems that consist of a small number of simple fuzzy rules with high comprehensibility. Those fuzzy systems are designed in a heuristic manner using rule selection criteria. We first describe fuzzy rule-based classification. Next we describe heuristic rule selection criteria using the terminology in data mining: confidence and support. A small number of fuzzy rules are extracted from numerical data based on each rule selection criterion. Then we examine the classification performance of extracted fuzzy rules through computational experiments on a number of benchmark data sets from the UCI ML Repository. Our results should be viewed as the lowest benchmark performance of fuzzy rule-based classification systems because fuzzy rules are extracted using a simple heuristic method with no optimization or tuning procedures. Nevertheless our results on some data sets are comparable to reported results by the C4.5 algorithm in the literature.

Induction of fuzzy rules and membership functions from training examples

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.

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy controllers is introduced. An optimization algorithm for designing the membership functions and fuzzy rule base for a fuzzy controller is presented. An optimal fuzzy controller using proposed technique for backing up a truck problem is designed and implemented, in which optimal fuzzy membership functions and fuzzy rules are designed. Also, a neural network controller for the same truck problem is designed to compare our results.

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

This paper introduces three hybrid methods for the generation and optimization of rules and membership functions of a fuzzy logic controller for nonlinear systems. The proposed methods overcome the deficiency of a systematic approach for optimal design of fuzzy controllers. An optimally designed fuzzy logic controller should have the least number of fuzzy variables and fuzzy rules and the best possible configuration of fuzzy rules in the rule table. The first strategy of this paper is a two-phase optimization problem: in the first phase, the number of fuzzy variables and their arrangement in the rule table are optimized by a genetic algorithm; in the second phase, the parameters of the membership functions are optimized via extended Kalman filtering. The second strategy tries to achieve the goals of the first method all in one phase by modifying the chromosome structure of the genetic algorithm. Then in the next step, a local search algorithm is utilized to improve the obtained results. The third strategy is similar to the second strategy in structure. However, along with optimizing the number of fuzzy variables and membership parameters, the number of fuzzy rules is also optimized. The first and second strategies are obliged to use every possible combination of fuzzy variables in the rule table; however, the third strategy is capable of distinguishing between useful and useless fuzzy rules in the rule table. The introduced strategies are applied to an automotive cruise control system. The results of the simulations show the effectiveness of the proposed methods and the superiority of the latter approaches over the former ones.

A genetic weighted fuzzy rule-based system is proposed in this paper, in which the parameters of membership functions including position and shape of the fuzzy rule set and weights of rules are evolved using a genetic algorithm. The efficiency of the system has been illustrated in the process of classifying the iris data. This paper also illustrates that compared with non-weighted fuzzy rules, weighted fuzzy rules can lead to better fuzzy system.

On the basis of PID engineering tuning methods, a fuzzy PID control algorithm based on the fuzzy rules is put forward. To design this kind of algorithm, firstly, simplify PID formula according to the Z-N and critical proportion methods, and then, construct a piece of fuzzy control table according to fuzzy control rules and fuzzy control reasoning. In the paper, three-dimensional fuzzy control is used, which is more predominant than that with two-dimension. In real-time control, by looking up fuzzy control table, the PID parameter can be adjusted on line. The simulation results of controlling the angle of the helm and course of the ship show that the algorithm raised in this paper has good performances such as simple operation, excellent personality, etc.

A method is proposed to design a wide-area damping controller (WADC) based on fuzzy control to dampen the low-frequency oscillations of interconnected power systems. First, the inputs and expected outputs of a fuzzy logic controller are analyzed. Then, a universe of fuzzy sets, membership functions and fuzzy rules are determined based on the relationship between inputs and outputs, and the fuzzy logic controller is constituted. The WADC consists of a fuzzy logic controller and a gain. The gain is obtained using particle swarm optimization. A four-machine two-area power system is simulated using the Matlab/Simulink software to test the performance of the fuzzy-based WADC. The simulation results indicate that the designed controller can compensate for communication delay and improve interconnected power system damping.