The Optimized Disign of the Fuzzy Controller(Ⅰ)——The predigested disquisition of rules of fuzzy control

To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.

On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions

In recent years, the use of Fuzzy set theory has been popularised for handling overlap domains in control engineering but this has mostly been within the context of triangular membership functions. In actual practice however, such domains are hardly triangular and in fact for most engineering applications the membership functions are usually Gaussian and sometimes cosine. In an earlier paper, we derived explicit Fourier series expressions for systematic and dynamic computation of grade of membership in the overlap and non-overlap regions of triangular Fuzzy sets. In another paper, we extended the methodology to cover cases of cosine, exponential and Gaussian Fuzzy sets by presenting explicit Fourier series representation for encoding fuzziness in the overlap and non-overlap domains of Fuzzy sets. This current paper presents the development of a “Fuzzy Controller” device, which incorporates the formal mathematical representation for computing grade of membership of Gaussian and triangular Fuzzy sets. It is shown that triangular approximation of Gaussian membership function in Fuzzy control can lead to wrong linguistic classification which may have adverse effects on operational and control decisions. The development of the Fuzzy controller demonstrates that the proposed technique can indeed be incorporated in engineering systems for dynamic and systematic computation of grade of membership in the overlap and non-overlap regions of Fuzzy sets;and thus provides a basis for the design of embedded Fuzzy controller for mission critical applications.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

A New Fuzzy Control Model for Kidney Patients

The final diagnosis of some diseases depends on many factors and information. Considering all these factors and reviewing all of them is a difficult process, thus providing a mathematical model that can simultaneously consider all these factors is a great help for physicians to diagnose and treat these diseases. In this paper, we propose a new fuzzy control model for kidney transplantation patients. For the inference and conclusion of this model, we use Mamdani approach. Also we employ a new method to smooth the well-known non-smooth piecewise membership functions, i.e. trapezoidal and half trapezoidal membership functions.

An Intelligent System for a Resource Allocation Problem Based on Fuzzy Reasoning

The development of the assistive abilities regarding the decision-making process o fan Intelligent Control System （ICS） like a fuzzy expert system implies the development of its functionality and its ability of specification. Fuzzy expert systems can model fuzzy controllers, i.e., the knowledge representation and the abilities of making decisions corresponding to fuzzy expert systems are much more complicated that in the case of standard fuzzy controllers. The expert system acts also as a supervisor, creating meta-level reasoning on a set of fuzzy controllers, in order to choose the best one for the management of the process. Knowledge Management Systems （KMSs） is a new development paradigm of Intelligent Systems which has resulted from a synergy between fuzzy sets, artificial neural networks, evolutionary computation, machine learning, etc., broadening computer science, physics, economics, engineering, mathematics. This paper presents, after a synergic new paradigm of intelligent systems, as a practical case study the fuzzy and temporal properties of knowledge formalism embedded in an ICS. We are not dealing high with level reasoning methods, because we think that real-time problems can only be solved by rather low-level reasoning. Solving the match-time predictability problem would allow us to build much more powerful reasoning techniques.

A NOVEL TEMPORAL ERROR CONCEALMENT METHOD BASED ON FUZZY REASONING FOR H.264

In this paper,a fuzzy reasoning based temporal error concealment method is proposed. The basic temporal error concealment is implemented by estimating Motion Vector (MV) of the lost MacroBlock (MB) from its neighboring MVs. Which MV is the most proper one is evaluated by some criteria. Generally,two criteria are widely used,namely Side Match Distortion (SMD) and Sum of Absolute Difference (SAD) of corresponding MV. However,each criterion could only partly describe the status of lost block. To accomplish the judgement more accurately,the two measures are considered together. Thus a refined measure based on fuzzy reasoning is adopted to balance the effects of SMD and SAD. Terms SMD and SAD are regarded as fuzzy input and the term ‘similarity’ as output to complete fuzzy reasoning. Result of fuzzy reasoning represents how the tested MV is similar to the original one. And k-means clustering technique is performed to define the membership function of input fuzzy sets adaptively. According to the experimental results,the concealment based on new measure achieves better performance.

Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System

This paper discusses the implementation of Load Frequency Control (LFC) in restructured power system using Hybrid Fuzzy controller. The formulation of LFC in open energy market is much more challenging;hence it needs an intelligent controller to adapt the changes imposed by the dynamics of restructured bilateral contracts. Fuzzy Logic Control deals well with uncertainty and indistinctness while Particle Swarm Optimization (PSO) is a well-known optimization tool. Abovementioned techniques are combined and called as Hybrid Fuzzy to improve the dynamic performance of the system. Frequency control of restructured system has been achieved by automatic Membership Function (MF) tuned fuzzy logic controller. The parameters defining membership function has been tuned and updated from time to time using Particle Swarm Optimization (PSO). The robustness of the proposed hybrid fuzzy controller has been compared with conventional fuzzy logic controller using performance measures like overshoot and settling time following a step load perturbation. The motivation for using membership function tuning using PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes is tested on a two-area restructured power system.

Optimization of Membership Function for Fuzzy Control Based on Genetic Algorithm and Its Applications

In this paper, a simple and practicable algorithm for optimization of membership function (MF) is proposed. As it is known that MF is very important in the fuzzy control. Unfortunately, to find, especially to optimize MF is always rather complex even difficult. So, to study and develop an effectual aglorithm for MF optimization is a good topic. Allow for the inner advantages of genetic algorithm (GA), it is adopted in the algorithm .The principle and executive procdeure are first presented. Then it is applied in the fuzzy control system of a typical plant. Results of real time run show that the control strategy is encouraging, and the developed algorithm is practicable.

Fuzzy logic controller design with unevenly-distributed membership function for high performance chamber cooling system

Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histogram equalization and noise detection were performed to modify the evenly-distributed membership functions of error and error change rate into unevenly-distributed membership functions.Then,the experimental results with evenly and unevenly distributed membership functions were compared under the same outside environment conditions.The experimental results show that the steady-state error is reduced around 40% and the noise disturbance is rejected successfully even though noise range is 60% of the control precision range.The control precision is improved by reducing the steady-state error and the robustness is enhanced by rejecting noise disturbance through the fuzzy logic controller with unevenly-distributed membership function.Moreover,the system energy efficiency and lifetime of electronic expansion valve(EEV) installed in chamber cooling system are improved by adopting the unevenly-distributed membership function.

Fuzzy Reasoning Methods by Choosing Different Fuzzy Counters and Analysis of Effect

Different fuzzy reasoning methods were gave by choosing different fuzzy counters.This article generally introduced the basic structure of fuzzy controller,and compared and analysised the reasoning effect of fuzzy reasoning methods and the effect of computer simulating control basicly on different fuzzy counters.

Asymptotically Necessary and Sufficient Quadratic Stability Conditions of T-S Fuzzy Systems Using Staircase Membership Function and Basic Inequality

Asymptotically necessary and sufficient quadratic stability conditions of Takagi-Sugeno (T-S) fuzzy systems are obtained by utilizing staircase membership functions and a basic inequality. The information of the membership functions is incorporated in the stability analysis by approximating the original continuous membership functions with staircase membership functions. The stability of the T-S fuzzy systems was investigated based on a quadratic Lyapunov function. The asymptotically necessary and sufficient stability conditions in terms of linear matrix inequalities were derived using a basic inequality. A fuzzy controller was also designed based on the stability results. The derivation process of the stability results is straightforward and easy to understand. Case studies confirmed the validity of the obtained stability results.

Fuzzy Logic for Solving an Optimal Control Problem of Hypoxemic Hypoxia Tissue Blood Carbon Dioxide Exchange during Physical Activity

This paper aims at using of an approach integrating the fuzzy logic strategy for hypoxemic hypoxia tissue blood carbon dioxide human optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of cardiovascular-respiratory system for a 30 years old woman in jogging as her regular physical activity. The results are in good agreement with experimental data.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

Fuzzy neural networks for arc welding quality control

Fuzzy Logic Control (FLC) is a promising control strategy in welding process control due to its ability for solving control problem with uncertainty as well as its independence on the analytical mathematics model. However, in basic FLC, the fuzzy rule relies heavily on the experts’ (e.g. advanced welders’) experience. In addition to this, the membership function for fuzzy set is non adaptive, i.e. it remains unchanged as long as they are determined by experience or other means. For welding process, which is time variable systems and strong disturbance exists in it, fixed membership function may not guarantee the required system performance, and attempts should be made to improve the system performance by adopting adaptive membership function. Therefore, the automatically determination of the fuzzy rule and in process adaptation of membership function are required for the advanced welding process control. This paper discussed the possibility by using the combination between FLC and neural network (NN) to realize the above propose. The adaptation of membership function as well as the self organizing of fuzzy rule are realized by the self learning and competitiveness of the NN. Taking GTAW process welds bead width regulating system as the controlled plant, the proposed algorithm was testified for such a process. Computer simulations showed the improvement of the system characteristics.

Fuzzy Logic Approach for Solving an Optimal Control Problem of an Uninfected Hepatitis B Virus Dynamics

We aimed in this paper to use fuzzy logic approach to solve a hepatitis B virus optimal control problem. The approach efficiency is tested through a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. Final results of both numerical methods are in good agreement with experimental data.

Fuzzy Mathematics for Raw Silk Size Control

With photographing and experiments, this paper divides the cocoon layers into three categories according to their colors, establishes three-color membership function based on fuzzy mathematics, constructs fuzzy sets which satisfy the range of size control by using the ordinary set and attached frequency of threecolor cocoons combination, then achieves the ordinary sets of range of size control by choosing λ-cut. Under these ordinary sets, each end does duality relative level, then sets up relative matrix and overall sequence and finds the membership function to judge whether the size control is mormal.

Fuzzy Logic Strategy for Solving an Optimal Control Problem of Glucose and Insulin in Diabetic Human

This paper aims at the development of an approach integrating the fuzzy logic strategy for a glucose and insulin in diabetic human optimal control problem. To test the efficiency of this strategy, the author proposes a numerical comparison with the indirect method. The results are in good agreement with experimental data.

Fuzzy Logic Strategy for Solving an Optimal Control Problem of Therapeutic Hepatitis C Virus Dynamics

This paper aims at the development of an approach integrating the fuzzy logic strategy for a therapeutic hepatitis C virus dynamics optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. The results are in good agreement with experimental data.

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

FUZZY控制理论在水泥机立窑煅烧过程的应用

针对水泥机立窑数学模型过于复杂且难以建立的特点，提出了以稳定底火为目标的微机模糊控制方案．对立窑底火进行了系统划分，给出了底火状态的模糊定义，并提出了一种多变量多级混合模型的识别方法．在总结人工操作经验的基础上经优化处理制定了稳定底火的模糊控制规则．为了简化计算过程，采用一种简单实用的模糊推理合成算法．计算机控制系统的实际运行表明了该控制方案的正确性和实用性．