基于神经网络的范例推理

目前对于基于范例推理的研究越来越受到人们的重视。本文探讨用神经网络来实现范例推理系统,用此方法建造一个高效的范例推理系统,并给出了一些算法。     

基于神经网络的范例推理

目前对于基于范例推理的研究越来越受到人们的重视.本文探讨用神经网络来实现范例推理系统,用此方法建造一个高效的范例推理系统,并给出了一些算法.     

基于归纳技术的范例推理及其应用

首先研究了可以与范例推理相结合的多种技术，并着重研究了基于范例推理和归纳技术的集成方法，以充分利用范例推理和归纳技术的各自优势，提高求解问题的能力。该文提出了一个基于归纳技术的范例推理分类算法，实验证明了此算法有着良好的分类准确率。     

用遗传算法挖掘范例库中的特征项权重的方法

范例推理的关键步骤在于相似范例的检索，而范例库中的特征项权重起着重要的作用。文章着重讨论了应用于范例库上获取特征项权重的数据挖掘算法，并提出用遗传算法发现范例库上特征项权重的过程与算法，然后进行了实验与讨论。     

用遗传模拟退火算法挖掘特征项权重的研究

能否在范例库中检索和选择出最为相似的范例决定了范例推理系统性能。文中介绍了遗传算法和模拟退火算法，比较了两种算法的特性．提出一种混合遗传模拟退火算法。该算法不但具有强的局部搜索能力．还缩短了搜索时间。将该算法用于发掘范例库上特征权重，理论分析和实验结果表明了这种混合遗传模拟退火算法优于普通的遗传算法。     

基于范例推理的CAD软件可重用技术的研究

基于软件重用技术与范例基技术在原理，在模具ＣＡＤ领域软件的工具箱式ＣＡＳＥ环境的研制中，通过范例基推理技术实现了ＣＡＤ软件可重用机制，提出了ＣＡＤ软件重用的范例定义，导出了范例推理算法，并给出范例推理可重用机制的功能模型。     

利用数据挖掘技术构造范例库的新方法探究

范例推理技术作为基于规则推理技术的补充,其关键就是能很好地解决知识获取的瓶颈问题,但在范例推理技术的实际应用中,如何高效建立范例库也是一个棘手的问题。采用数据挖掘技术,提出一种综合算法从传统数据库中构造范例库,可望部分解决范例获取的自动化问题,提高系统的运行效率及整体性能。     

范例推理中的知识发现技术

范例推理中有许多相关的知识 ,相应地有知识获取过程 ,其中也存在一定程度的知识获取瓶颈问题 .本文着重探讨在范例推理系统中引入一系列可以使用的知识发现技术 ,以期提高范例推理系统的知识获取的自动化程度 ;本文针对提出的两类算法 ,进行了实验与讨论     

基于范例推理的CAD软件可重用技术的研究

基于软件重用技术与范例基技术在原理，在模具CAD领域软件的工具箱式CASE环境的研制中，通过范例基推理技术实现了CAD软件可重用机制，提出了CAD软件重用的范例定义，导出了范例推理算法，并给出范例推理可重用机制的功能模型。     

用遗传模拟退火算法挖掘特征项权重的研究

能否在范例库中检索和选择出最为相似的范例决定了范例推理系统性能。文中介绍了遗传算法和模拟退火算法,比较了两种算法的特性,提出一种混合遗传模拟退火算法。该算法不但具有强的局部搜索能力,还缩短了搜索时间。将该算法用于发掘范例库上特征权重,理论分析和实验结果表明了这种混合遗传模拟退火算法优于普通的遗传算法。     

MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.     

Case-based reasoning and neural network based expert system for personalization

We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains.We propose feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers’ character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.     

A hybrid case-based model for forecasting

An investigation is described into the application of artificial intelligence to forecasting in the domain of oceanography. A hybrid approach to forecasting the thermal structure of the water ahead of a moving vessel is presented which combines the ability of a case-based reasoning system for identifying previously encountered similar situations and the generalizing ability of an artificial neural network to guide the adaptation stage of the case-based reasoning mechanism. The system has been successfully tested in real time in the Atlantic Ocean; the results obtained are presented and compared with those derived from other forecasting methods.     

多层前馈神经网络在基于案例推理的应用

基于案例的推理（CBR）系统的增量式学习会使案例库逐渐增大,导致案例的检索时间较长,效率较低。多层前馈神经网络是构造性神经网络技术,很容易构筑及理解,具有较低的时间和空间复杂性和较高的识别率。利用该神经网络技术对案例库进行分类后,待求解的新问题只需在某个子案例库中进行检索,便可以有效地解决大规模案例库的能力与效率的维护问题,确保CBR系统的能力保护与效率保护兼顾的实现,为大规模案例库的应用提供技术保证。     

加热炉生产数据预处理策略研究

加热炉在钢铁企业发挥着非常重要的作用. 在加热炉中部分生产过程数据较难检测, 部分检测到的数据受到严重干扰和缺失, 这严重影响了加热炉的优化和控制, 而且还存在潜在的安全隐患. 本文针对加热炉这一复杂的过程, 设计了一个生产过程数据预处理系统. 该系统能对部分难以测量的数据用自适应模糊神经网络(FNN)方法进行预测, 能对过程数据进行滤波和正误判断, 能对异常数据进行剔除和替代, 并对过程数据替代值利用案例推理(CBR)方法建立完善机制. 该系统在某钢铁公司进行了实际应用, 取得了明显的应用效果.     

基于多层前馈神经网络的案例推理系统

采用基于该神经网络技术的案例推理系统，使用交叉覆盖算法，可兰亨登地缩减案例的检索时间、减少案例适应性修改、提高推磊效率。实验表明该系统易于设计构建，极大地提升了CBR在实际中的应用能力。     

Case-DeSC: A system for case-based design with soft computing techniques

The present paper introduces a case-based design with soft computing (Case-DeSC) system that uses soft computing techniques for addressing parametric design problems. Design case representation relies on digraphs of design parameters, supported by fuzzy preferences on specific parameters’ values and weighting factors, which capture the parameters’ relative importance. The final design solution is either extracted via a genetic algorithm that searches for the solution with the maximum aggregated preference, or it is retrieved by a competitive neural network. This neural network utilizes the medium of the maximum or the centroid of the assigned fuzzy preferences as similarity measures and it is trained by utilizing the available cases in the case base. Several functionalities are incorporated to the proposed system (case selection through aggregation of fuzzy preferences, case adaptation through genetic optimization with retrieved solutions used as initial population, multi-layered neural networks trained with retrieved cases used for adaptation tasks etc.). The system is evaluated through an example case of parametric design of an oscillating conveyor.     

Feature Weight Maintenance in Case Bases Using Introspective Learning

A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications.Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.     

基于敏感度分析的案例特征项权重算法的改进

研究案例库特征项权重的确定方法,通过集成BP神经网络和敏感度分析,改进案例库特征项的权重确定算法,将案例库中的各特征项和决策目标项构造一个BP神经网络,经训练和学习后,依次删除输入节点,分析网络的输出对输入的敏感程度,确定各特征项的权重。并以红籽西瓜仁重的案例库对其进行测试,结果表明该算法是有效的。