基于免疫规划的神经网络规则抽取算法

建立图像视觉特征和情感语义的映射关系是人工智能方向的研究热点。从神经网络的功能性观点出发,提出了一种基于免疫规划的图像情感的规则抽取算法。在对已标注情感的中国情绪图片库(CAPS)中图像颜色特征进行量化的基础上,算法将训练好的神经网络的隐层神经元输出值进行聚类,缩小搜索空间,抽取出精度高,可理解性好的符号规则,完成了图像低阶特征到高阶情感的映射。实验结果表明该方法的实用性和可行性。     

神经网络模糊规则抽取及其应用

从功能的观点出发,提出了一种基于粒子群优化算法的神经网络模糊规则抽取方法。该方法利用所要抽取模糊规则的表达形式,设计了规则的粒子三段表示方式,在粒子群算法优化过程中,采用两种更新方法,即离散化方法和连续化方法。该方法不依赖于具体的网络结构和训练算法,可以方便地应用于各种回归型神经网络。仿真实验表明,该方法可以抽取出保真度较高的符号规则。在实际应用中,采用模糊规则抽取算法,从丙烯腈反应器软测量模型中所得到的规则,提供了一种参数调节的指导性策略。     

一种基于统计的神经网络规则抽取方法

从功能性观点出发,提出了一种基于统计的神经网络规则抽取方法.该方法利用统计技术对抽取出的规则进行评价,使其可以较好地覆盖示例空间.采用独特的连续属性处理方式,降低了离散化处理的主观性和复杂度.采用优先级规则形式,不仅使得规则表示简洁、紧凑,而且还免除了规则应用时所需要的一致性处理.该方法不依赖于具体的网络结构和训练算法,可以方便地应用于各种分类器型神经网络.实验表明,利用该方法可以抽取出可理解性好,简洁、紧凑,保真度高的符号规则.     

基于文化免疫克隆算法的关联规则挖掘研究

针对关联规则挖掘问题,给出一种基于文化免疫克隆算法的关联规则挖掘方法,该方法将免疫克隆算法嵌入到文化算法的框架中,采用双层进化机制,利用免疫克隆算法的智能搜索能力和文化算法信念空间形成的公共认知信念的引导挖掘规则。该方法重新给出了文化算法中状况知识和历史知识的描述,设计了一种变异算子,能够自适应调节变异尺度,提高免疫克隆算法全局搜索能力。实验表明,该算法的运行速度和所得关联规则的准确率优于免疫克隆算法。     

神经网络规则抽取研究

尽管神经网络已经在很广泛的领域得到应用,但由于训练好的神经网络中的知识不易于理解,神经网络被视为一个典型的“黑箱”结构。从神经网络中抽取规则来表示其中隐含的知识是解决个问题的一个有效的手段,将对一些具有代表性的神经网络规则抽取算法进行综述分析,并提出一些未来的研究重点。     

基于域理论的自适应谐振神经网络研究

提出了一种基于域理论的自适应谐振神经网络算法 FTART,有机结合了自适应谐振理论和域理论的优势 ,以一种独特的方式解决了示例间冲突和分类区域的动态扩展 ,不仅不需要手工设置隐层神经元 ,可以还获得了较快的训练速度和较高的预测精度 .同时还提出了一种可以从训练好的 FTART网络中抽取可理解性好、精度高的符号规则的方法 ,即基于统计的产生测试法 .实验结果表明 ,用该方法抽取的符号规则可以较好地描述FTART的功能.     

神经网络规则提取及其在特征带识别中的应用

基于神经网络的规则提取通常只是针对某些特定的数据集，缺乏领域专家的鉴别，实际应用效果难以判定。本文针对分子标记特征带识别这一实际问题，在准确率损失尽量小的情况下剪枝训练好的神经网络，利用同符号规则提取算法（SSRE）抽取规则，避免了繁琐的规则抽取过程。实验表明此方法的效果良好，结果得到生物专家的认可。     

图像情感语义规则抽取的研究

针对图像视觉特征和情感语义之间的语义鸿沟,以图像纹理为低层特征,通过使用BP神经网络完成了图像低层特征到情感语义的映射;并在精度保持不变的前提下,对训练好的网络模型进行剪枝,最后通过神经网络规则抽取算法将隐含在神经网络模型中的知识转化为易于理解的IF-THEN规则形式。实验验证了方法的有效性和规则的可理解性。     

基于BP神经网络的小麦病害诊断知识获取

为了从神经网络中获取易于理解的知识,以小麦病害诊断为例,研究了BP神经网络的规则抽取,提出一种基于结构分析的BP神经网络规则抽取方法.采用带惩罚项的交错熵误差函数作为误差函数,通过对训练好的神经网络进行剪枝、权重分析并设定阈值,从BP神经网络中抽取产生式规则.     

神经网络规则抽取评估方法

神经网络规则抽取是神经网络领域的一个重要方向,但是对抽取的规则评估算法却很少.针对这一问题,提出了神经网络抽取规则评估方法.首先证明所有的规则形式都可以统一为区间的形式,然后在区间算法的基础上提出规则评估方法.评估的标准有四个:覆盖性、准确性、矛盾性,以及冗余性.由于规则的矛盾性和冗余性是规则之间的问题,所以该文仅仅研究规则的覆盖性和准确性,提出了覆盖性判断定理,并提出了覆盖性、准确性判断算法.实例证实了该算法的有效性.     

Reorganizing knowledge in neural networks: an explanatory mechanismfor neural networks in data classification problems

We propose an explanatory mechanism for multilayered neural networks (NN). In spite of the effective learning capability as a uniform function approximator, the multilayered NN suffers from unreadability, i.e., it is difficult for the user to interpret or understand the "knowledge" that the NN has by looking at the connection weights and thresholds obtained by backpropagation (BP). This unreadability comes from the distributed nature of the knowledge representation in the NN. In this paper, we propose a method that reorganizes the distributed knowledge in the NN to extract approximate classification rules. Our rule extraction method is based on the analysis of the function that the NN has learned, rather than on the direct interpretation of connection weights as correlation information. More specifically, our method divides the input space into "monotonic regions" where a monotonic region is a set of input patterns that belongs to the same class with the same sensitivity pattern. Approximate classification rules are generated by projecting these monotonic regions.     

一种基于多物种协同进化的神经网络优化方法

论文在阐明了遗传算法和神经网络结合的必要性之后,分析了一般遗传算法在神经网络结构优化过程中存在的不足,并根据多物种之间相互竞争和相互适应的机理提出了一种基于多物种协同进化的优化方法。该方法既可以有效地避免神经网络结构寻优过程中解搜索空间过大以及进化规则复杂等问题,还可以起到对网络的结构和权值同时进化的作用。仿真实验表明该方法是可行并且有效的。     

Training a neural network to select dispatching rules in real time

Dispatching rules are often suggested to schedule manufacturing systems in real-time. Numerous dispatching rules exist. Unfortunately no dispatching rule (DR) is known to be globally better than any other. Their efficiency depends on the characteristics of the system, operating condition parameters and the production objectives. Several authors have demonstrated the benefits of changing dynamically these rules, so as to take into account the changes that can occur in the system state. A new approach based on neural networks (NN) is proposed here to select in real time, each time a resource becomes available, the most suited DR. The selection is made in accordance with the current system state and the workshop operating condition parameters. Contrarily to the few learning approaches presented in the literature to select scheduling heuristics, no training set is needed. The NN parameters are determined through simulation optimization. The benefits of the proposed approach are illustrated through the example of a simplified flow-shop already published. It is shown that the NN can automatically select efficient DRs dynamically: the knowledge is only generated from simulation experiments, which are driven by the optimization method. Once trained offline, the resulting NN can be used online, in connection with the monitoring system of a flexible manufacturing system.     

Recursive neural network rule extraction for data with mixed attributes

In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.     

融合语法规则的双通道中文情感模型分析

针对使用中文文本进行情感分析时,忽略语法规会降低分类准确率的问题,提出一种融合语法规则的双通道中文情感分类模型CB_Rule.首先设计语法规则提取出情感倾向更加明确的信息,再利用卷积神经网络(CNN)的局部感知特点提取出语义特征;然后考虑到规则处理时可能忽略上下文的问题,使用双向长短时记忆(Bi-LSTM)网络提取包含...     

On the design of neural-fuzzy control system

This article presents a neural–network-based fuzzy logic control (NN–FLC) system. The NN–FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NN–FLC model. Computer simulations of the inverted pendulum controlled by NN–FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.13: 11–26, 1998     

An expert system for detection of breast cancer based on association rules and neural network

This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR + NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR + NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

基于神经网络的交互式自动获取知识的N－R方法

本文为解决知识系统构造过程中的瓶颈问题──知识获取，提出了一种基于神经网络ＮＮ（ｎｅｕｒａｌｎｅｔｗｏｒｋ）的自动获取多级推理产生式规则的Ｎ－Ｒ方法，该方法采用了特有的ＮＮ结构模型和相应的学习算法，使得ＮＮ在学习过程中动态确定隐层节点数的同时，也产生了样例集中没有定义的新概念，学习后的ＮＮ能用本文提出的转换算法转换成推理网络，最终方便地得到产生式规则集.