A learning process for fuzzy control rules using genetic algorithms

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the thrid one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable genetic algorithms.     

迎接e时代的品质挑战

本文先介绍e时代来临与特质,e时代对品质的新需求，e时代的品质展开，e时代的学习曲线，以及如何应对e时代的品质挑战。     

Supervised fuzzy pattern recognition

This paper is devoted to the problem of supervised fuzzy pattern recognition. The cases with non-fuzzy and fuzzy labels are considered. Based on the properties of linearly separable fuzzy classes, some algorithms are proposed for building matching functions of these classes. All algorithms are computer oriented and can be implemented for the automatic recognition of fuzzy patterns.     

The powers and pitfalls of algorithmic knowledge: a case study

This study examines one child's use of computational procedures over a period of 3 years in an urban elementary school where teachers were using a standards-based curriculum. From a sociocultural perspective, the use of standard algorithms to solve mathematical problems is viewed as a cultural tool that both enables and constrains particular practices. As this student appropriated and mastered procedures for addition, subtraction, multiplication and division, she could solve problems that involved fairly straightforward computations or where she could easily model the action to determine an appropriate computation. At the same time, her use of these algorithms, along with other readily available tools, such as her fingers or multiplication tables, constrained her ability to reflect on the tens-structure of the number system, an effect that had serious consequences for her overall mathematical achievement. The results of this study suggest that even when not directly introduced, algorithms have such strong currency that they can mediate more reform-oriented instruction.     

基于Web的协同学习策略及其应用

对网络教育的现状进行了分析，并针对目前网络教育中师生之间、学生之间缺乏有效的交流的缺点，提出了将协同学习策略应用在的基于Web的协同学习系统中的方法。使用多种同学习策略对协同学习过程进行控制，并且将学习评估与协同学习过程进行结合，以提高学生的学习效果。     

Analog VLSI Circuits for Competitive Learning Networks

An investigation is made concerning implementations of competitive learning algorithms in analog VLSI circuits and systems. Analog and low power digital circuits for competitive learning are currently important for their applications in computationally-efficient speech and image compression by vector quantization, as required for example in portable multi-media terminals. A summary of competitive learning models is presented to indicate the type of VLSI computations required, and the effects of weight quantization are discussed. Analog circuit representations of computational primitives for learning and evaluation of distortion metrics are discussed. The present state of VLSI implementations of hard and soft competitive learning algorithms are described, as well as those for topological feature maps. Tolerance of learning algorithms to observed analog circuit properties is reported. New results are also presented from simulations of frequency-sensitive and soft competitive learning concerning sensitivity of these algorithms to precision in VLSI learning computations. Applications of these learning algorithms to unsupervised feature extraction and to vector quantization of speech and images are also described.     

入侵检测系统中基于变量相似性特征选择

ReliefF是一种在很多场合经常使用的filter式的特征选择方法．然而该方法的一大缺点是不能辨别冗余特征。基于ReliefF算法提出一种混合的有监督的特征选择算法。该算法首先利用ReliefF算法去除与分类无关的以及权重低于一定阈值的特征，然后采用一种变量相似性准则来去除冗余特征。在实际的数据集KDDCUP＇99上进行的实验结果表明该混合特征选择方法较单独使用ReliefF方法在分类精度上有一定的提高。     

手写汉字的四角识别方案

手写汉字的本质特征的抽取是提高识别率的关键，而识别规则的生成又依赖于大量样本的归纳学习，手写汉字的四角方案就是针对这两个问题设计并实现的。实验结果证明了该方法的可行性和优越性。     

人工神经网络的容量、学习与计算复杂性

本文讨论了人工神经网络（ＡＮＮ）解决问题的能力，从广泛的角度讨论了容量问题，推广与学习问题，深入研究了ＡＮＮ通过学习解决问题的计算复杂性，以及解决实际问题时困难所在。