Jeoung-Nae Choi

Identification of fuzzy models using a successive tuning method with a variant identification ratio

The paper is concerned with the identification of fuzzy inference systems (fuzzy models) realized with the aid of the successive tuning method using a variant identification ratio that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFC-PGA) and information granulation. The HFC-PGA is a certain multi-population version of Parallel Genetic Algorithms (PGA), which is suitable for a simultaneous optimization of both the structure of the fuzzy model as well as its parameters. The granulation of information is realized with the aid of the C-Means clustering algorithm. Information granules formed in this way become essential at further stages of the construction of the fuzzy models by forming the centers (modal values) of the fuzzy sets constituting individual rules of the inference schemes. Further optimization of the fuzzy model deals with an adjustment of a suite of parameters (such as the number of input variables to be used in the model, a collection of specific subsets of the input variables, and the number of membership functions) being used by these variables, and the order and parameters of the polynomial occurring in the conclusions of the corresponding rules. An iterative development of the fuzzy model deals with its structural as well as parametric optimization via HFC-PGA, the C-Means algorithm, and a standard least square estimation method. To evaluate the performance of the proposed model, we exploit well known and commonly used data sets such as gas furnace, Mackey-Glass time series as well as medical imaging system. A comparative analysis demonstrates that the proposed model leads to the superior performance when compared with other fuzzy models reported in the literature.

A tuning algorithm for the PID controller utilizing fuzzy theory

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, /spl tau//sub I/, /spl tau//sub D/, by the Ziegler-Nichols formula (1942) that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and find the new proportion gain (Kp) and the integral time (/spl tau//sub I/) from fuzzy tuner by the error and error rate of plant output as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant properties of this algorithm is shown by simulation.

Analytic design of information granulation‐based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

Purpose – The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization realized by means of the Multiobjective Particle Swarm Optimization (MOPSO).Design/methodology/approach – In fuzzy modeling, complexity, interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. Since the performance of the IG‐RBFNN model is directly affected by some parameters, such as the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials in the consequent parts of the rules, the authors carry out both structural as well as parametric optimization of the network. A multi‐objective Particle Swarm Optimization using Crowding Distance (MOPSO‐CD) as well as O/WLS learning‐based optimization are exploited to carry out the structural and parametric optimization of the model, respectively, while the optimization is of multiobjective character as i...

Hybrid optimization of information granulation‐based fuzzy radial basis function neural networks

Purpose – The purpose of this paper is to propose an improved optimization methodology of information granulation‐based fuzzy radial basis function neural networks (IG‐FRBFNN). In the IG‐FRBFNN, the membership functions of the premise part of fuzzy rules are determined by means of fuzzy c‐means (FCM) clustering. Also, high‐order polynomial is considered as the consequent part of fuzzy rules which represent input‐output relation characteristic of sub‐space and weighted least squares learning is used to estimate the coefficients of polynomial. Since the performance of IG‐RBFNN is affected by some parameters such as a specific subset of input variables, the fuzzification coefficient of FCM, the number of rules and the order of polynomial of consequent part of fuzzy rules, we need the structural as well as parametric optimization of the network. The proposed model is demonstrated with the use of two kinds of examples such as nonlinear function approximation problem and Mackey‐Glass time‐series data.Design/met...

Design of Optimized Fuzzy Controller by Means of HFC-based Genetic Algorithms for Rotary Inverted Pendulum System

In this paper, we propose an optimized fuzzy controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for rotary inverted pendulum system. We adopt fuzzy controller to control the rotary inverted pendulum and the fuzzy rules of the fuzzy controller are designed based on the design methodology of Linear Quadratic Regulator (LQR) controller. Simple Genetic Algorithms (SGAs) is well known as optimization algorithms supporting search of a global character. There is a long list of successful usages of GAs reported in different application domains. It should be stressed, however, that GAs could still get trapped in a sub-optimal regions of the search space due to premature convergence. Accordingly the parallel genetic algorithm was developed to eliminate an effect of premature convergence. In particular, as one of diverse types of the PGA, HFCGA has emerged as an effective optimization mechanism for dealing with very large search space. We use HFCGA to optimize the parameter of the fuzzy controller. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy controller leads to superb performance in comparison with the conventional LQR controller as well as SGAs based fuzzy controller.

Simultaneous Optimization of ANFIS-Based Fuzzy Model Driven to Data Granulation and Parallel Genetic Algorithms

The paper concerns the simultaneous optimization for structure and parameters of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. HFCGA is used to optimize structure and parameters of ANFIS-based fuzzy model simultaneously. The granulation is realized with the aid of the C-means clustering. Through the simultaneous optimization mechanism to be explored, we can find the overall optimal values related to structure as well as parameter identification of ANFIS-based fuzzy model via HFCGA, C-Means clustering and standard least square method. A comparative analysis demon-strates that the proposed algorithm is superior to the conventional methods.

Design of Optimized Fuzzy PD Cascade Controller Based on Parallel Genetic Algorithms

In this paper, we propose the design of an optimized fuzzy cascade controller for rotary inverted pendulum system by means of Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) which is a kind of parallel genetic algorithms. The rotary inverted pendulum system is the system for controlling the inclination of pendulum axis through the adjustment of rotating arm. The control objective of the system is to control the position of rotating arm and to make the pendulum maintain the unstable equilibrium point of vertical position. To control rotary inverted pendulum system, we designs the fuzzy cascade controller scheme consisted of two fuzzy controllers and optimizes the parameters of the designed controller by means of HFCGA. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller leads to superb performance in comparison with the conventional LQR controller as well as HFCGA based PD cascade controller.

Optimization of Fuzzy Model Driven to IG and HFC-Based GAs

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) and information data granulation. HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. The granulation is realized with the aid of the Hard C-means clustering (HCM). The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly organized based on center points of data group extracted by the HCM clustering. It concerns the fuzzy model-related parameters such as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the polynomial type of the consequence part of fuzzy rules. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

Optimization of information granulation-oriented fuzzy set model using hierarchical fair competition-based parallel genetic algorithms

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on information granulation and Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA and HCM method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

Design of Optimized Multi-Fuzzy Controllers for Air-Conditioning System with Multi-Evaporators

In this paper, we introduce an approach to design multi-fuzzy controllers for the superheat and the low pressure that have an influence on energy efficiency and stabilization of aft conditioning system. Air conditioning system is composed of compressor, condenser several evaporators and several expansion valves. It is quite difficult to control the air conditioning system because the change of the refrigerant condition give an impact on the overall air conditioning system. In order to solve the drawback, we design multi-fuzzy controllers which control simultaneously both three expansion valve and one compressor for the superheat and the low pressure of air conditioning system. The proposed multi fuzzy controllers are given as two kinds of controller types such as a continuous simplified fuzzy inference type and a discrete fuzzy lookup_table type. Here the scaling factors of each fuzzy controller ate efficiently adjusted by veal coding type Genetic Algorithms. The values of performance index of the conventional type are compared with the simulation results of discrete lookup_table type and continuous simplified inference type.