A new error estimation approach based on fuzzy logic system for H-R adaptive boundary element method

Conventional adaptive boundary element method cannot be universally applied to solve many more problems than the subject it discussed, and different error estimation formulas need to be designed for varied problems. This paper put forward a new error analysis method based on the fuzzy logic system, which is able to make error estimation effectively using human expert experience, and solve the two classical elasticity problems in conjunction with the H-R adaptive boundary element method. Numerical examples have illustrated the effectiveness, superiority and potential of a fuzzy logic approach in the adaptive boundary element method.     

基于样条插值的模糊控制算法

利用三次样条插值函数,直接由控制输入输出数据对建立了控制输入与控制输出之间的映射关系,得到了一元三次样条插值控制算法和二元双三次样条插值控制算法,并将二者分别用于单输入单输出系统和双输入单输出系统的仿真控制.仿真结果表明,上述方法是可行的,并且基于三次样条函数的模糊插值控制,具有响应快,无超调,稳态误差极小等很好的控制效果.其设计简单,不需要过多规则,对稀疏规则库条件下的控制器设计尤为适用.     

Fuzzy projection pursuit density estimation by eigenvalue method

This paper focuses on the use of kernel method and projection pursuit regression for non-parametric probability density estimation. Direct application of the kernel method is not able to pick up characteristic features of multidimensional density function. We propose a fuzzy projection pursuit density estimation based on the membership function and the eigenvector of the covariance matrix. Marginal densities along the subspace spanned by the projection vector are estimated. The proposed projection pursuit is one of the methods which are able to bypass the ‘curse of dimensionality’ in multidimensional density estimation. An application to experimental design for machining accuracy of end milling with the tool in small diameter is presented to demonstrate its usefulness.     

State estimation of neural networks with both time-varying delays and norm-bounded parameter uncertainties via a delay decomposition approach

This paper is concerned with the state estimation problem for neural networks with both time-varying delays and norm-bounded parameter uncertainties. By employing a delay decomposition approach and a convex combination technique, we obtain less conservative delay-dependent stability criteria to guarantee the existence of desired state estimator for the delayed neural networks. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed approach.     

The availability model and parameters estimation method for the delay time model with imperfect maintenance at inspection

The delay time model (DTM) is widely used to model the two-stage failure process and is helpful for developing cost-effective inspection/maintenance plans. Imperfect maintenance is common in practice, but seldom considered in DTM. An improved DTM with imperfect maintenance at inspection has been developed based on the assumption of imperfect inspection maintenance and perfect failure maintenance. The model of the long-run availability for the improved DTM is established. Parameters estimation method and the test for goodness of fit method are given. Numerical simulations are performed to study the influence of imperfect maintenance on the long-run availability and to validate the credibility of the parameters estimation method. The results show that imperfect maintenance will decrease the long-run availability. The existence of the optimal inspection interval regarding the maximum long-run availability is tightly related to the improvement factor, which denotes the maintenance effect. The parameters estimation method proves credible. The maximum likelihood estimations of the reliability parameters can be easily achieved by the Genetic Algorithms (GAs) searching tool.     

Fuzzy uncertainty modeling for grid based localization of mobile robots

This paper presents a localization method using fuzzy logic to represent the different facets of uncertainty present in sensor data. Our method follows the typical predict-update cycle of recursive state estimators to estimate the robot’s location. The method is implemented on a fuzzy position grid, and several simplifications are introduced to reduce computational complexity. The main advantages of this fuzzy logic method compared to most current ones are: (i) only an approximate sensor model is required, (ii) several facets of location uncertainty can be represented, and (iii) ambiguities in the sensor information are directly represented, thus avoiding having to solve the data association problem separately. Our method has been validated experimentally on two different platforms, a legged robot equipped with vision and a wheeled robot equipped with range sensors. The experiments show that our method can solve both the tracking and the global localization problem. They also show that this method can successfully cope with ambiguous observations, when several features may be associated to the same observation, and with robot kidnapping situations. Additional experiments are presented that compare our approach with a state-of-the-art probabilistic method.     

A fuzzy inverse relation based on godelian logic and its applications

Fuzzy logic applicable to decision-making processes such as diagnostic expert systems and fuzzy controllers is the prime concern of this paper. In these applications, an inverse operation (or backward inference) plays an important role. In the current paper, a new backward inference technique based on Gödelian logic is proposed. The relation between the forward and backward inferences is established by introducing fuzzy joint and conditional relations.     

Design of fuzzy logic controllers based on generalized T-operators

Since Zadeh first proposed the basic principle of fuzzy logic controllers in 1968, the and operators have been popular in the design of fuzzy logic controllers. In this paper, the general concept of T-operators is introduced into the conventional design methods for fuzzy logic controllers so that a general and flexible methodology for the design of these fuzzy logic controllers is available. Then, by computer simulations, studies are made so as to determine the relations between the various T-operators and the performance of a fuzzy logic controller. It is concluded that the performance of the fuzzy logic controller for a given class of plants very much depends upon the choice of the T-operators.     

Control and synchronizing nonlinear systems with delay based on a special matrix structure

This work presents a direct approach to design stabilizing controller for nonlinear systems with delay based on a special matrix structure and proves the validity of the approach according to Lyapunov–Krasovskii stable theorem and Linear Matrix Inequality—LMI. Control Lorenz system and synchronizing Rössler system with delay are taken as examples to explain the approach. Numerical simulations confirm the effectiveness of the approach proposed.     

Chaos synchronization using fuzzy logic controller

The design of a rule-based controller for a class of master-slave chaos synchronization is presented in this paper. In traditional fuzzy logic control (FLC) design, it takes a long time to obtain the membership functions and rule base by trial-and-error tuning. To cope with this problem, we directly construct the fuzzy rules subject to a common Lyapunov function such that the master–slave chaos systems satisfy stability in the Lyapunov sense. Unlike conventional approaches, the resulting control law has less maximum magnitude of the instantaneous control command and it can reduce the actuator saturation phenomenon in real physic system. Two examples of Duffing–Holmes system and Lorenz system are presented to illustrate the effectiveness of the proposed controller.     

Operational matrices of Chebyshev cardinal functions and their application for solving delay differential equations arising in electrodynamics with error estimation

In this paper, a new and effective direct method to determine the numerical solution of pantograph equation, pantograph equation with neutral term and Multiple-delay Volterra integral equation with large domain is proposed. The pantograph equation is a delay differential equation which arises in quite different fields of pure and applied mathematics, such as number theory, dynamical systems, probability, mechanics and electrodynamics. The method consists of expanding the required approximate solution as the elements of Chebyshev cardinal functions. The operational matrices for the integration, product and delay of the Chebyshev cardinal functions are presented. A general procedure for forming these matrices is given. These matrices play an important role in modelling of problems. By using these operational matrices together, a pantograph equation can be transformed to a system of algebraic equations. An efficient error estimation for the Chebyshev cardinal method is also introduced. Some examples are given to demonstrate the validity and applicability of the method and a comparison is made with existing results.     

A new technique for linear static state estimation based on weighted least absolute value approximations

This paper presents a new technique for solving the problem of linear static state estimation, based on weighted least absolute value (WLAV). A set ofm optimality equations is obtained, wherem=number of measurements, based on minimizing a WLAV performance index involvingn unknown state variables,m>n. These equations are solved using the left pseudo-inverse transformation, least-square sense, to obtain approximately the residual of each measurement.Ifk is the rank of the matrixH,k=n, we choose among the optimality equations a number of equations equal to the rankk and having the smallest residuals. The solution of thesen equations inn unknowns yields the best WLAV estimation. A numerical example is reported; the results for this example are obtained by using both WLS and WLAV techniques. It is shown that the best WLAV approximation is superior to the best WLS approximation when estimating the true form of data containing some inaccurate observations.This work was supported by the Natural Science and Engineering Research Council of Canada, Grant No. A4146.     

Finding an inner estimation of the solution set of a fuzzy linear system

In this paper, we consider the problem of finding an inner estimation of the solution set of a fuzzy linear system with a real-valued coefficient matrix and a fuzzy-valued right-hand side vector. The proposed idea is based on the utilization of interval Gaussian elimination procedure to produce an inner estimation of the solutions set. To this end, firstly we apply interval Gaussian elimination procedure to obtain the solution set of a fuzzy linear system and secondly, by limiting it via solving a crisp linear system, we find an inner estimation of the solutions set, such that it satisfies the related fuzzy linear system. Finally, several numerical examples are given to show the efficiency and ability of our method.     

H∞ control of a class of networked control systems with time delay and packet dropout

This paper studies the H-infinity control issue for a class of networked control systems (NCSs) with time delay and packet dropout. The state feedback closed-loop NCS is modeled as a discrete-time switched system. Through using a Lyapunov function, a sufficient condition is obtained, under which the system is exponential stability with a desired H-infinity disturbance attenuation level. The designed H-infinity controller is obtained by solving a set of linear matrix inequalities. An illustrative example is presented to demonstrate the effectiveness of the proposed method.     

Parameter estimation for noisy chaotic systems based on an improved particle swarm optimization algorithm

It is of great importance to estimate the unknown parameters and time delays of chaotic systems in control and synchronization. This paper is concerned with the uncertain parameters and time delays of chaotic systems corrupted with random noise. Parameters and time delays of such chaotic systems are estimated based on the improved particle swarm optimization algorithm for its global searching ability. Numerical simulations are given to show satisfactory results.     

基于Elmeri指标体系的煤矿作业区安全风险模糊综合评价

2016年起我国推行煤矿安全风险分级管控与隐患排查治理双重预防机制,各煤矿积极探索自主管理之路,如何将作业区域的风险辨识与总体评价有机结合,建立综合评价模型是值得探讨的课题.本文给出一种便捷的安全风险模糊评价模型首先基于Elmeri指标体系构建了安全的行为、健康危险源、事故危险源、重大事故控制和应急准备5个一级指标和15个二级指标,在作业区进行现场观察,计算出安全指数作为风险管控的依据,采用层次分析法确定指标权重采用梯形分布确定隶属度函数,在总体评价中确定了好、较好、一般、差四个模糊评价等级,根据M(.,+)模型和最大隶属度原则确定作业区域安全风险等级,并在煤矿进行现场应用,该模型方法简单,将定量的风险辨识数据与综合评价有机结合起来,评价结果与实际相符,值得在国内外推广.