并行遗传算法与神经网络,模糊系统的结合

遗传算法是模拟自然界生物进化过程的计算模型。本文介绍了并行遗传算法的不同分类及不同并行策略，又将遗传算法分别与神经网络、模糊系统结合起来进行并行处理，并在曙光１０００系统上实现。算法分析表明，并行遗传算法可以有效地提高收敛速度。     

复杂系统的广义预测模糊控制

针对多变量复杂系统提出一种基于广义预测与模糊控制相集成的控制策略。控制器具有多层结构与智能控制特点，不仅计算量小、实时性强，而且具有良好的“解耦”和动态跟踪性能，明显地提高了该类系统的闭环控制效果。     

泥沙随机起动的简化模式

泥沙的起动既有随机性，又有模糊性，据此作者将模糊数学方法引入泥沙起动研究。建立了直接反映泥沙随机起动特性的起动流速计算模式，该模式得到了实测资料的检验。这一模式可以体现泥沙起动的随机性，较好地反映了泥沙起动的本来面目     

On fuzzy sobriety

This paper mainly shows that sober fuzzy topological spaces form the epireflective hull of the fuzzy Sierpinski space in the category of T₀-fuzzy topological spaces.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

Colour classification by neural networks in graphic arts

This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected. Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.     

Robust Estimation of Rotation Angles from Image Sequences Using the Annealing M-Estimator

A robust method is presented for computing rotation angles of image sequences from a set of corresponding points containing outliers. Assuming known rotation axis, a least-squares (LS) solution are derived to compute the rotation angle from a clean data set of point correspondences. Since clean data is not guaranteed, we introduce a robust solution, based on the M-estimator, to deal with outliers. Then we present an enhanced robust algorithm, called the annealing M-estimator (AM-estimator), for reliable robust estimation. The AM-estimator has several attractive advantages over the traditional M-estimator: By definition, the AM-estimator involves neither scale estimator nor free parameters and hence avoids instabilities therein. Algorithmically, it uses a deterministic annealing technique to approximate the global solution regardless of the initialization. Experimental results are presented to compare the performance of the LS, M- and AM-estimators for the angle estimation. Experiments show that in the presence of outliers, the M-estimator outperforms the LS estimator and the AM-estimator outperforms the M-estimator.     

遥感图像的模糊边缘检测技术

本文结合遥感图像的实际背景，给出了可用于操作实施的模糊边缘检测数学模型和算法。     

pH过程的模糊控制仿真研究

pH过程广泛存在于化工、生物工艺、污水处理、发电等工业过程中，对这一过程进行良好的控制有着十分重要的意义。由于pH过程是一个具有严重非线性及滞后性的被控对象，传统的PID控制难以达到理想的控制效果。本文使用模糊控制技术，设计采用了带有自调整因子的模糊控制器，不仅能较好地处理pH过程的严重非线性和滞后性，而且具有较强的鲁棒性和抗干扰能力。     

A fuzzy constraint satisfaction approach for electronic shopping assistance

The Internet and World Wide Web offer an additional channel for consumers to find, select, and buy products. However, unlike shopping in the traditional store, consumers have no direct contact with human clerks to get the required information in the electronic store. The objective of this paper is to propose a fuzzy constraint satisfaction approach to help buyers find fully satisfactory or replacement products in electronic shopping. For the buyer who can give precise product requirements, the proposed approach can generate product-ranking lists based on the satisfaction degrees of each product to the given requirements. For the buyer who may not input accurate requirements, a similarity analysis approach is proposed to assess buyer requirements automatically during his browsing process. The proposed approach could help buyers find the preferred products on the top of the ranking list without further searching the remaining pages. The experimental results show the applicability of the proposed approach for electronic shopping assistance.