模糊遗传算法在敏感词分类优化中的应用

针对以往敏感词分类优化的不足,提出一种基于模糊遗传算法的敏感词分类优化方法,该方法把模糊逻辑理论用于遗传算法,模拟生物进化过程和机制来求解实际的敏感词定性结构优化问题。研究表明,对于敏感词词性以及结构的变化有很好的分类优化效果,从而保证了整体的分类质量、快速的分类效率、鲁棒和可靠的分类性能。     

基于QPSO的模糊分类系统优化设计

提出了构建模糊分类系统的有效方法.通过量子位选择的方法对初始的模糊规则进行优化,减少种群规模、提高全局搜索能力,且可以大幅缩短训练时间,达到快速收敛、有效分类的目的.为了优化模糊分类空间和减少模糊规则数目,提出了量子行为粒子群优化(QPSO)算法,提高初始模糊分类系统的性能.实验结果证明:优化方法较之其他方法更有效率,准确率更高.     

一种基于支持向量机的模糊分类系统研究

提出一种基于支持向量机学习的模糊分类束纯模型．通过将支持向量机映射成等价的模糊分类系统,支持向量机的稀疏性表示等特性使得相应的模糊分类系统避免了“维数灾难”问题,并具有良好的泛化能力．另一方面,模糊系统的一些理论和应用成果也可用来进一步改善分类系统的性能．本文根据模糊集合的贴近度概念对模糊系统的语言变量进行约简,合并冗余的和不一致的模糊规则,然后采用粒子群优化方法改善模糊分类系统性能．该方法增强了系统的泛化能力,并可以理解为解决支持向量机中难以确定的系统参数问题的一种辅助方法．实验结果表明了该方法的可行性和有效性．     

模糊动态环境下复杂系统的满意优化控制

提出一种在满意控制框架下进行模糊决策的方法,将控制目标和系统约束模糊化,形 成多目标的优化问题,通过模糊规划方法求解,与基于二次型性能指标的预测控制相比,该方法 可使得系统设计更灵活.     

基于SA的模糊分类系统在数据挖掘中的应用

为了找到模糊分类规则的优化集,以改善与数据挖掘中分类问题有关的数据探索与开拓的性能,提出了在分类问题中利用模拟退火(SA)技术.对构建模糊分类器的SA元启发搜索机制进行了研究,该搜索机制能够从输入数据集中抽取精确的模糊if-then规则.在UCI数据集上用计算机进行了模拟,实验结果表明了基于模拟退火的模糊分类系统对于分类输入向量的鲁棒性.     

模糊优化算法及其在视觉机器人路径规划中的应用

提出了视觉机器人路径规划的模糊满意优化方法.该算法基于预测控制滚动优化机制,将系统优化目标和受限约束通过模糊隶属度来表示,形成多目标模糊优化问题,解决了在全局环境未知情况下的优化路径问题,仿真结果验证了该方法的有效性.     

一种基于多目标进化算法的模糊关联分类方法

准确率和解释性是模糊关联分类模型的两个相互制约的优化目标.目前已有的研究方法中,有的只考虑了分类模型的准确率,有的把模型两个目标转化为单目标问题求解,在模型解释性目标上的优化策略较简单.为此提出一种基于Apriori和NSGA-II多目标进化算法的模糊关联分类模型(MOEA-FACM),采用基于概率独立性的模糊确认指标筛选生成高质量的模糊关联规则集,以Pittsburgh式的编码方式构建准确率和解释性折中的模糊关联分类模型.标准数据集上的实验表明,该方法所建模型分类准确率比同类模型高,分类模型具有较好的泛化能力,而其所含模糊关联规则的数目和规则前件总的模糊项的个数却较少,模型的解释性较好.     

一种基于模糊分类的Apriori优化算法

针对海量数据挖掘过程中常见Apriori算法的弊病,提出一种基于模糊分类的Apriori优化算法.它平滑地解决了用区间划分方法处理量化属性存在的问题,避免了经典Apriori算法带来的巨大性能开销.以职称考试成绩为例,验证了该算法的有效性.     

多变量模糊优化信息可视化研究

本文针对复杂工程系统的多变量模糊优化问题进行了可视化方法研究，提出了模糊子域的绘制与运算算法，并在此基础上，对多变量模糊优化问题进行了数学分析，直观显示出了模糊可行域在设计空间上的分布规律．     

模糊优化算法及其在视觉机器人路径规划中的应用

提出了视机器人路径规划的模糊满意优化方法，该算法基于预测控制滚动优化机制，将系统优化目标和受限约束通过模糊隶属度来表示，形成多目标模糊优化问题，解决了在全局环境未知情况下的优化路径问题。仿真结果验证了该方法的有效性。     

Approach to decision making in fuzzy environment

A general approach to solving a wide class of optimization problems with fuzzy coefficients in objective functions and constraints is described. It is based on a modification of traditional mathematical programming methods and consists in formulating and solving one and the same problem within the framework of interrelated models with constructing equivalent analogs with fuzzy coefficients in objective function alone. This approach allows one to maximally cut off dominated alternatives from below as well as from above. The subsequent contraction of the decision uncertainty region is associated with reduction of the problem to multicriteria decision making in a fuzzy environment. The approach is applied within the context of fuzzy discrete optimization models, that is based on a modification of discrete optimization algorithms. The results of the paper are of a universal character and are already being used to solve problems of the design and control of power systems and subsystems.     

A new support vector machine based on type-2 fuzzy samples

Classical support vector machine is based on the real valued random samples and established on the probability space. It is hard to deal with classification problems based on type-2 fuzzy samples established on non-probability space. The existing algorithm, type-2 fuzzy support vector machine established on generalized credibility space, transforms the classification problems based on type-2 fuzzy samples to general fuzzy optimization problems and expands the application range of traditional support vector machine. However, nonnegativeness of the decision variables of general fuzzy optimization problems is too strict to be satisfied in some practical applications. Motivated by this, the concept of expected fuzzy possibility measure is proposed. Then type-2 fuzzy support vector machine on expected fuzzy possibility space is established, and the second-order cone programming of type-2 fuzzy support vector machine on expected fuzzy possibility space is given. The results of numerical experiments show the effectiveness of the type-2 fuzzy support vector machine established on expected fuzzy possibility space.     

Fuzzy rules extraction from support vector machines for multi-class classification

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. SVMs have been used in a variety of applications. However, they are considered “black box models,” where no interpretation about the input–output mapping is provided. Some methods to reduce this limitation have already been proposed, but they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. In order to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. The proposed model is suited for classification in multi-class problems and includes a wrapper feature selection algorithm. It is evaluated in four benchmark databases, and results obtained demonstrate its capacity to generate a reduced set of interpretable fuzzy rules that explains both the classification database and the influence of each input variable on the determination of the final class.     

Parallel machine scheduling in fuzzy environment with hybrid ant colony optimization including a comparison of fuzzy number ranking methods in consideration of spread of fuzziness

This paper studies parallel machine scheduling problems in consideration of real world uncertainty quantified based on fuzzy numbers. Although this study is not the first to study the subject problem, it advances this area of research in two areas: (1) Rather than arbitrarily picking a method, it chooses the most appropriate fuzzy number ranking method based on an in-depth investigation of the effect of spread of fuzziness on the performance of fuzzy ranking methods; (2) It develops the first hybrid ant colony optimization for fuzzy parallel machine scheduling. Randomly generated datasets are used to test the performance of fuzzy ranking methods as well as the proposed algorithm, i.e. hybrid ant colony optimization. The proposed hybrid ant colony optimization outperforms a hybrid particle swarm optimization published recently and two simulated annealing based algorithms modified from our previous work.     

Performance evaluation of fuzzy classifier systems formultidimensional pattern classification problems

We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems. Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some well-known test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.     

Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm

In the present paper, a genetic algorithm for multi-objective optimization problems with max-product fuzzy relation equations as constraints is presented. Since the non-empty feasible domain of such problems is, in general, a non-convex set; the traditional optimization methods cannot be applied. Here, we are presenting a genetic algorithm (GA) to find “Pareto optimal solutions” for solving such problems observing the role of non-convexity of the feasible domain of decision problem. Solutions are kept within feasible region during the mutation as well as crossover operations. Test problems are developed to evaluate the performance of the proposed algorithm and to determine satisficing decisions. In case of two objectives, weighting method is also applied to find the locus of optimal solutions.     

Classification of fuzzy data based on the support vector machines

Data may be afflicted with uncertainty. Uncertain data may be shown by an interval value or in general by a fuzzy set. A number of classification methods have considered uncertainty in features of samples. Some of these classification methods are extended version of the support vector machines (SVMs), such as the Interval‐SVM (ISVM), Holder‐ISVM and Distance‐ISVM, which are used to obtain a classifier for separating samples whose features are interval values. In this paper, we extend the SVM for robust classification of linear/non‐linear separable data whose features are fuzzy numbers. The support of such training data is shown by a hypercube. Our proposed method tries to obtain a hyperplane (in the input space or in a high‐dimensional feature space) such that the nearest point of the hypercube of each training sample to the hyperplane is separated with the widest symmetric margin. This strategy can reduce the misclassification probability of our proposed method. Our experimental results on six real data sets show that the classification rate of our novel method is better than or equal to the classification rate of the well‐known SVM, ISVM, Holder‐ISVM and Distance‐ISVM for all of these data sets.     

交流电机系统中的模糊控制研究

本文对模糊控制的理论方法目前在交流电机系统中的应用作了全面的分析和总结． 针对交流电机系统控制的特点，重点讨论了简单模糊控制、复合模糊控制以及仿生模糊控制 等几种典型的模糊控制方法和技术，分析了它们各自的特点和存在的问题，并给出了模糊控 制的实现方法和手段．最后，对模糊控制在交流电机系统中应用的关键问题和未来发展方向 进行了展望．     

Standby redundancy optimization problems with fuzzy lifetimes

Three types of system performances—the expected system lifetime, α-system lifetime, and system reliability—characterized in the context of credibility are investigated in this paper. Some fuzzy simulations are designed to estimate these system performances. In order to formulate general standby redundancy optimization problems with fuzzy lifetimes, a spectrum of standby redundancy fuzzy programming models are proposed. Fuzzy simulation, neural network, and genetic algorithm are also integrated to produce a hybrid intelligent algorithm for solving those models. Finally, some numerical experiments on multi-stage system and network system are provided.