指数威布尔分布参数的模糊最小二乘估计

讨论了当寿命数据含有异常数据时指数威布尔分布参数的估计问题。当寿命数据含有异常数据时,利用基于模糊聚类的最小二乘方法对参数进行估计,这种方法能削弱异常数据的影响。并获得了在完全样本情形下参数的最小二乘估计和加权最小二乘估计。最后给出数值模拟,结果显示模糊最小二乘估计是正确有效的。     

A new family of life distributions for dependent data: Estimation

One of the biggest problems in reliability analysis is determining an appropriate distribution of life data. Therefore, this paper develops the estimation aspect of a family of life distributions obtained from spherical distributions. Additionally, a new family of life distributions is proposed for dependent life data, together with an optimization algorithm based on the simulated annealing method. This algorithm is very efficient for optimization purposes and does not require any manipulation of the log-likelihood functions for the distributions proposed in this study.     

A new family of life distributions for dependent data: Estimation

One of the biggest problems in reliability analysis is determining an appropriate distribution of life data. Therefore, this paper develops the estimation aspect of a family of life distributions obtained from spherical distributions. Additionally, a new family of life distributions is proposed for dependent life data, together with an optimization algorithm based on the simulated annealing method. This algorithm is very efficient for optimization purposes and does not require any manipulation of the log-likelihood functions for the distributions proposed in this study.     

Estimation of continuous-time linear system parameters from periodic data

The parameters of a continuous-time linear system are identified by use of integral functions satisfying periodicity conditions. Explicit equations are derived for the evaluation of integral functions in terms of measured periodic output data. It is shown that a Walsh function representation of the integral functions has modest computational requirements making implementation on a 16-bit microprocessor feasible. Simulation and experimental results are provided to illustrate truncation error, estimation accuracies, and output response errors.     

Estimation of parameters from progressively censored data using EM algorithm

EM algorithm is used to determine the maximum likelihood estimates when the data are progressively Type II censored. The method is shown to be feasible and easy to implement. The asymptotic variances and covariances of the ML estimates are computed by means of the missing information principle. The methodology is illustrated with two popular models in lifetime analysis, the lognormal and Weibull lifetime distributions.     

TSK fuzzy model with minimal parameters

In this paper, an efficient genetic algorithm (GA) to generate a simple and well defined TSK model is proposed. The approach is derived from the use of the improved Strength Pareto Evolutionary Algorithm (SPEA-2), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. In our approach, we first apply the back-propagation algorithm to optimize the parameters of the model (parameters of membership functions and fuzzy rules), then we apply the SPEA-2 to optimize the number of fuzzy rules, the number of parameters and to fine tune these parameters.Two well-known dynamic benchmarks are used to evaluate the performance of the proposed algorithm. Simulation results show that our modeling approach outperforms some methods proposed in previous work.     

Safety life analysis under required failure credibility constraint for unsteady thermal structure with fuzzy input parameters

Structural and Multidisciplinary Optimization - To measure the safety degree of the unsteady thermal structure under fuzzy uncertainty, the time-dependent failure credibility (TDFC) is presented....     

Multi-objective aggregate production planning with fuzzy parameters

In this paper, a direct solution method that is based on ranking methods of fuzzy numbers and tabu search is proposed to solve fuzzy multi-objective aggregate production planning problem. The parameters of the problem are defined as triangular fuzzy numbers. During problem solution four different fuzzy ranking methods are employed/tested. One of the primary objectives of this study is to show that how a multi-objective aggregate production planning problem which is stated as a fuzzy mathematical programming model can also be solved directly (without needing a transformation process) by employing fuzzy ranking methods and a metaheuristic algorithm. The results show that this can be easily achieved.     

Univariate statistical analysis with fuzzy data

Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy. Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be considered. Moreover Bayesian inference methods in the situation of fuzzy a priori information and fuzzy data are discussed.     

Linear programming approach for solving fuzzy critical path problems with fuzzy parameters

To the best of our knowledge, there is no method in the literature to find the fuzzy optimal solution of fully fuzzy critical path (FFCP) problems i.e., critical path problems in which all the parameters are represented by LR flat fuzzy numbers. In this paper, a new method is proposed for the same. Also, it is shown that it is better to use JMD representation of LR flat fuzzy numbers in the proposed method as compared to the other representation of LR flat fuzzy numbers.     

Estimation in bivariate nonnormal distributions with stochastic variance functions

Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.     

On some lifetime distributions with decreasing failure rate

A new two-parameter distribution family with decreasing failure rate arising by mixing power-series distribution and exponential distribution is introduced. This family includes some well-used mixing distributions. Various properties of this family are discussed and the estimation of parameters are obtained by method of maximum likelihood. An EM algorithm is proposed for computing the estimates and expression for their asymptotic variances and covariances are derived. Simulation studies are performed and experimental results are illustrated based on real data sets.     

含有模糊和随机参数的混合机会约束规划模型

提出一类混合机会约束规划模型，该模型同时含有模糊和随机参数，运用随机模拟与模糊模拟相结合的技术，给出了求解该机会约束规划模型的遗传算法，通过对生产过程最优化决策的典型问题进行分析建模和数值求解，说明了该模型和算法的合理性和有效性。     

带有模糊参数的应急物资筹集问题决策模型

应急物资筹集决策是应急物流系统重要研究问题之一,是继应急物资需求预测之后的一项重要应急物资管理活动,它直接关系到应急物流目标的实现。建立了仅动用库存条件下应急物资筹集决策模糊优化模型,利用模糊机会约束规划的思想,给出了与其等价的模糊机会约束规划模型,在此基础上提出了应急物资筹集决策模型的算法,最后通过算例说明了该方法的有效性。     

具有数据容错能力的模糊C均值聚类算法

提出基于模糊c均值聚类算法的两个新算法.设置每个数据隶属度的误差阈值,规定每个数据的隶属度误差不能超过给出的误差阈值.使用该类算法可以对有误差的数据进行模糊聚类.先利用隶属度矩阵的误差范围建立新的拉格朗日函数,再使用Kuhn-Tucker条件计算该函数,并通过一组实验来证明这类算法的正确性和有效性.     

Intelligent data analysis with fuzzy decision trees

Intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.     

Fault-tolerant scheduling on parallel systems with non-memoryless failure distributions

As large parallel systems increase in size and complexity, failures are inevitable and exhibit complex space and time dynamics. Most often, in real systems, failure rates are increasing or decreasing over time. Considering non-memoryless failure distributions, we study a bi-objective scheduling problem of optimizing application makespan and reliability. In particular, we determine whether one can optimize both makespan and reliability simultaneously, or whether one metric must be degraded in order to improve the other. We also devise scheduling algorithms for achieving (approximately) optimal makespan or reliability. When failure rates decrease, we prove that makespan and reliability are opposing metrics. In contrast, when failure rates increase, we prove that one can optimize both makespan and reliability simultaneously. Moreover, we show that the largest processing time (LPT) list scheduling algorithm achieves good performance when processors are of uniform speed. The implications of our findings are the accelerated completion and improved reliability of parallel jobs executed across large distributed systems. Finally, we conduct simulations to investigate the impact of failures on the performance, which is done using an actual application of biological sequence comparison.