基于粗糙集与前馈网络的案例智能系统的研究
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摘要
学习是智能行为的最重要体现，从古希腊人开始，对知识的研究与探索，一直是人类追求的目标。类比是人类重要的认知方法，是一种允许知识在具有相似性质的领域中进行推理的学习策略，是人类直觉、逻辑与创造性三种思维方式的综合表现形式，也是人们经验决策过程中常用的思维方式。案例推理技术作为智能系统一种新的推理方法，是人脑类比学习的计算机实现，也是这一领域研究的先行者和成功的实践者。对案例智能系统的研究，有助于对人类思维的模仿，实现人类智能。
从大量数据中获取知识、表达知识以及推理决策规则，是智能信息处理的首要任务，特别是对于实际问题中不确定、不完备知识的处理，粗糙集理论和人工神经网络技术都展示出惊人数据处理的能力。本文围绕实现案例推理循环的主要过程，以粗糙集为主体的综合推理和前馈神经网络的大规模数据处理为基础，研究提高案例推理系统的精度和效率、增强系统的柔性和鲁棒性。本文的主要研究内容如下：
(1)案例智能系统的知识表示以案例为基础，案例表示可能是半结构化或非结构化的、甚至用自然语言来表达的；研究案例推理的类比可行性、案例推理的逻辑基础和推理的知识结构，并给出了人类知识推理中的一些困惑；以及实现案例智能，必须要处理好的知识结构等几个方面的问题。构建合适的案例库，如何对它进行组织与维护，对快速、有效地完成案例的检索是十分重要的，对问题求解的性能有直接影响。
(2)从知识推理的角度对不确定知识的研究模型及其关系进行分析，详细研究了粗糙集与案例推理相结合的综合推理技术，多方面寻找理论模型融合的技术和方法，从而便于我们从更高的层次理解人类思维及其问题处理方法，从不完备知识中推出本质知识，这对高层决策是至关重要的。
从问题求解的实际需要出发，综合推理技术可以充分运用多层次的知识，将多种推理技术集成，采用不同的知识粒度，提高系统推理的效率。最终实现构建统一的、更加有效的、能够处理复杂的和模糊的信息的粒计算的理论平台，从而为案例智能系统的实现提供了可靠的技术基础，并极大地提高了系统的问题处理能力；最后提出了一种案例智能决策支持系统的体系结构。
(3)研究了智能知识检索技术，并详细研究了前馈神经网络及其作为案例检索的算法；指出目前仍在广泛使用的BP算法、模拟退火算法及其改进算法，具有难以克服的速度慢、局部极值的缺点。随后对比研究了径向基函数算法，它是根据模式矢量的多维空间距离的非线性影射来识别和分类的，是一个良好的相似性检测器。研究表明RBF网络具有学习速度快、全局收敛等优点；然后建立了基于RBF网络的案例相似检索模型。
(4)详细研究构造性神经网络及其算法，将网络的某个性能的优劣作为算法追求的目标之一进行考虑，从全局来考察神经网络的学习过程，研究构造性的网络结构对于解决大规模的问题求解有重要的意义。从M-P神经元的几何意义入手，对比早期的构造性神经网络算法——FP算法，然后推出了覆盖算法。研究表明该技术易于模块化构建具有快速、识别率高等优点，最后给出了基于构造性神经网络的领域覆盖算法的案例智能系统。
(5)详细研究了案例知识库维护技术，提出了案例知识库维护的原则。案例推理是增量式学习系统，类似人类的知识积累。案例库维护是CBR知识系统研究的核心，涉及CBR推理的知识表示、适配与改写过程；虽然CBM作为CBR研究的一个重要分支，已经广为研究并开发出来不同的案例库维护策略；但是在不同的环境下，因CBR系统的规模、时效性以及应用领域的特点不同，案例知识库维护手段和维护性能存在较大的差异。
针对冗余和不一致案例知识，或者由于领域知识变化的环境，提出了基于相似粗糙集的案例知识库维护技术，基于知识粒度，做到可控阈值的实时监测、实现实时维护。
针对实际领域应用中如故障诊断、在线帮助、电子商务等交互式状态下，其案例库的规模很容易达到成千上万条且不可约简的系统性能维护难题，提出了基于覆盖算法的案例知识库维护，系统同时从两方面着手完成案例库维护：一方面用覆盖算法将案例库划分成覆盖领域，实现案例的选择性过滤使用；另一方面应用多层前馈神经网络改进案例匹配，提高检索效率。实验表明该方法可以用来处理海量的高维数据，保证了系统的可用性。
Learning is the most important embodiment of human intelligence, approaching to research and investigation on knowledge, has been the goal that mankind constantly engage for since ancient Greek. Analogy is an important cognitive model of human sense, and also a kind of inferential study strategy allowing people to process knowledge reasoning course wherever they have similar characters. Cases are the integrated representation of human sense, logics and creativity, and naturally become a common artifice when people process experiential decision-making. As a new reasoning technique to construct intelligent systems, case-based reasoning (CBR) performing in the computer, is the great achievement for the simulation of human analogy learning. No doubt it becomes a forthgoer in the research of human analogy sense, and a successful practitioner in these investigations. Study case intelligent system is helpful to imitate human thinking and achieve a simulator of human intelligence.
The primary task for intelligent information processing is how to acquire knowledges, express them and drill rules for decision-making from huge amounts of data. Especially for the dealing with the real-world problems full of uncertainty, incomplete knowledge, Rough Sets (RS) theory and Artificial Neural Networks (ANN) technology have been emerging an amazing capacity. Centering round how to accomplish key CBR processes with these tremendous data, the dissertation therefore puts forward synthesis reasoning techniques mainly on the basis of RS and Multilayer Feed Forward Neural Networks (MFNN), and focuses on how to improve CBR system's precision with high efficiency, enlarge its flexibility besides robustness. The dissertation mainly deals with the following items.
Firstly, cases are regarded as the foundation for knowledge representation in case intelligent system, and may be represented in semi-structured, unstructured model, or even in natural language text. The dissertation studies the feasibility of analogy from logics and reasoning structure for CBR process, and gives some potential perplexities about human's knowledge reasoning chains. To implement case intelligence, some aspects besides knowledge structure must be well managed. Those problems, such as how to construct suitable case base, organize cases and maintain them, are very important influencing factors to acquire the best problem-solving cases from the former quickly and efficiently, when they solve new problems in case retrieval, undoubtedly they are strongly interrelated with the system efficiency in problem-solving.
Secondly, the dissertation studies the current main reseaching models on uncertain knowledge and analyzes their relationships from the view of knowledge chains, so that we can comprehend human sense and its problem-solving method from a higher level. In search of the models or methods to perform those theories fusion with methods, the dissertation investigates synthesis reasoning technology mainly combined with CBR and RS from many aspects. The findings indicate that effective synthesis reasoning contributes to an essential knowledge repository vital for incomplete knowledge discovery to make informed strategic decisions.
According to the real problem-solving demands, synthesis reasoning can combine various reasoning principles and integrate many methods; consequently it can apply many kinds of knowledge from various levels, and enhance the system's efficiency of reasoning by means of adaptable knowledge granularity. Gradually it shall tend to set up a universal granular computing platform, which can deal with complex and fuzzy information more efficient, and provide reliable techniques in construction case intelligent systems, enlarging the system's ability of problem-solving, a framework of case-intelligent DSS is therefore figured out.
Thirdly, the dissertation studies intelligent case retrieval techniques. After investigating the behavior of MFNN together with many kinds of existent algorithms for case retrieval based on MFNN, besides widely used back propagation algorithm (BP), simulated annealing algorithm and their ameliorated algorithms, it points out that weaknesses such as having lower speed and local extreme value, are inherent in those algorithms, thus cannot be conquered thoroughly. Compared with those former algorithms, radial basis function network (RBF), which recognizes and classifies samples dependence on their non-linear distance through projecting them to a multi dimensional space, is a good similarity detector. The subsequent research indicates that RBF network has so many advantages like fast learning and global convergence, then the dissertation puts forward a model for similar case retrieval based on RBF network.
Fourthly, the dissertation investigates constructive neural networks together with their learning methods, and masters the learning course of neural networks from overall viewpoint. Any algorithm shall engage with certain characters of the neural network, which is selected as one of the purpose and should be taken care of. It is a significant leap to study constructive neural networks, which is capable of large-scale problem solving. The study begins with the MP neuron model from the view of geometry, and traces into covering algorithm (CA) from forward propagation algorithm (FP, compared with BP), the former algorithm of constructive neural networks. The sequent investigations indicate that CA has quite a number of characters, for example, clear system structure, feasible component for combination, running fast company with high recognizing ratio, etc., and it can be easily integrated and constructed. Then, the dissertation puts forward a model-case intelligent system-based on constructive neural networks and covering algorithm.
Finally, the dissertation investigates case knowledge base maintenance and puts forward some criteria for this cycle. The main knowledge base of the CBR system is the case library, and its learning ability is continuously transacting adding new cases, just as people accumulate their knowledge. The knowledge implicit in case library is involved in CBR process, such as case representation, case matching and case adaptation, thus case base maintenance (CBM) becomes one of the key problem in CBR research. As an important branch of the CBR community, CBM has developed some kinds of methods, but they should be restricted to their running conditions, such as CBR system's scale, aging effect even the working area, and CBM methods must be adapted for any changes.
Considering redundant cases or inconsistent knowledge possibly caused by the changing environment, the dissertation puts forward a CBM strategy based on Similarity Rough Set technique, which can achieve real-time maintenance, and implement a controllable threshold according to the selected granularity as a real-time monitor.
Aiming to the efficiency of case retrieval in the irreducible case library, for that CBR systems running in interactive domains like e-commerce, online helpdesk, fault diagnosis etc., and can easily reach thousands of cases, the dissertation puts forward another CBM strategy based on covering algorithm and MFNN to achieve CBM from both sides: One is employing Alternative-Covering Algorithm to partition the case library to many Covering Domains and thus realizing the selective filtering; the other is using MFNN to deal with case retrieval within the large-scale case library. Our experimental results indicate that the integrated method, which is especially feasible for the processing of vast, multiclass and high dimensional data, can effectively guarantee the system's usability and enhance its capability.
从大量数据中获取知识、表达知识以及推理决策规则，是智能信息处理的首要任务，特别是对于实际问题中不确定、不完备知识的处理，粗糙集理论和人工神经网络技术都展示出惊人数据处理的能力。本文围绕实现案例推理循环的主要过程，以粗糙集为主体的综合推理和前馈神经网络的大规模数据处理为基础，研究提高案例推理系统的精度和效率、增强系统的柔性和鲁棒性。本文的主要研究内容如下：
(1)案例智能系统的知识表示以案例为基础，案例表示可能是半结构化或非结构化的、甚至用自然语言来表达的；研究案例推理的类比可行性、案例推理的逻辑基础和推理的知识结构，并给出了人类知识推理中的一些困惑；以及实现案例智能，必须要处理好的知识结构等几个方面的问题。构建合适的案例库，如何对它进行组织与维护，对快速、有效地完成案例的检索是十分重要的，对问题求解的性能有直接影响。
(2)从知识推理的角度对不确定知识的研究模型及其关系进行分析，详细研究了粗糙集与案例推理相结合的综合推理技术，多方面寻找理论模型融合的技术和方法，从而便于我们从更高的层次理解人类思维及其问题处理方法，从不完备知识中推出本质知识，这对高层决策是至关重要的。
从问题求解的实际需要出发，综合推理技术可以充分运用多层次的知识，将多种推理技术集成，采用不同的知识粒度，提高系统推理的效率。最终实现构建统一的、更加有效的、能够处理复杂的和模糊的信息的粒计算的理论平台，从而为案例智能系统的实现提供了可靠的技术基础，并极大地提高了系统的问题处理能力；最后提出了一种案例智能决策支持系统的体系结构。
(3)研究了智能知识检索技术，并详细研究了前馈神经网络及其作为案例检索的算法；指出目前仍在广泛使用的BP算法、模拟退火算法及其改进算法，具有难以克服的速度慢、局部极值的缺点。随后对比研究了径向基函数算法，它是根据模式矢量的多维空间距离的非线性影射来识别和分类的，是一个良好的相似性检测器。研究表明RBF网络具有学习速度快、全局收敛等优点；然后建立了基于RBF网络的案例相似检索模型。
(4)详细研究构造性神经网络及其算法，将网络的某个性能的优劣作为算法追求的目标之一进行考虑，从全局来考察神经网络的学习过程，研究构造性的网络结构对于解决大规模的问题求解有重要的意义。从M-P神经元的几何意义入手，对比早期的构造性神经网络算法——FP算法，然后推出了覆盖算法。研究表明该技术易于模块化构建具有快速、识别率高等优点，最后给出了基于构造性神经网络的领域覆盖算法的案例智能系统。
(5)详细研究了案例知识库维护技术，提出了案例知识库维护的原则。案例推理是增量式学习系统，类似人类的知识积累。案例库维护是CBR知识系统研究的核心，涉及CBR推理的知识表示、适配与改写过程；虽然CBM作为CBR研究的一个重要分支，已经广为研究并开发出来不同的案例库维护策略；但是在不同的环境下，因CBR系统的规模、时效性以及应用领域的特点不同，案例知识库维护手段和维护性能存在较大的差异。
针对冗余和不一致案例知识，或者由于领域知识变化的环境，提出了基于相似粗糙集的案例知识库维护技术，基于知识粒度，做到可控阈值的实时监测、实现实时维护。
针对实际领域应用中如故障诊断、在线帮助、电子商务等交互式状态下，其案例库的规模很容易达到成千上万条且不可约简的系统性能维护难题，提出了基于覆盖算法的案例知识库维护，系统同时从两方面着手完成案例库维护：一方面用覆盖算法将案例库划分成覆盖领域，实现案例的选择性过滤使用；另一方面应用多层前馈神经网络改进案例匹配，提高检索效率。实验表明该方法可以用来处理海量的高维数据，保证了系统的可用性。
Learning is the most important embodiment of human intelligence, approaching to research and investigation on knowledge, has been the goal that mankind constantly engage for since ancient Greek. Analogy is an important cognitive model of human sense, and also a kind of inferential study strategy allowing people to process knowledge reasoning course wherever they have similar characters. Cases are the integrated representation of human sense, logics and creativity, and naturally become a common artifice when people process experiential decision-making. As a new reasoning technique to construct intelligent systems, case-based reasoning (CBR) performing in the computer, is the great achievement for the simulation of human analogy learning. No doubt it becomes a forthgoer in the research of human analogy sense, and a successful practitioner in these investigations. Study case intelligent system is helpful to imitate human thinking and achieve a simulator of human intelligence.
The primary task for intelligent information processing is how to acquire knowledges, express them and drill rules for decision-making from huge amounts of data. Especially for the dealing with the real-world problems full of uncertainty, incomplete knowledge, Rough Sets (RS) theory and Artificial Neural Networks (ANN) technology have been emerging an amazing capacity. Centering round how to accomplish key CBR processes with these tremendous data, the dissertation therefore puts forward synthesis reasoning techniques mainly on the basis of RS and Multilayer Feed Forward Neural Networks (MFNN), and focuses on how to improve CBR system's precision with high efficiency, enlarge its flexibility besides robustness. The dissertation mainly deals with the following items.
Firstly, cases are regarded as the foundation for knowledge representation in case intelligent system, and may be represented in semi-structured, unstructured model, or even in natural language text. The dissertation studies the feasibility of analogy from logics and reasoning structure for CBR process, and gives some potential perplexities about human's knowledge reasoning chains. To implement case intelligence, some aspects besides knowledge structure must be well managed. Those problems, such as how to construct suitable case base, organize cases and maintain them, are very important influencing factors to acquire the best problem-solving cases from the former quickly and efficiently, when they solve new problems in case retrieval, undoubtedly they are strongly interrelated with the system efficiency in problem-solving.
Secondly, the dissertation studies the current main reseaching models on uncertain knowledge and analyzes their relationships from the view of knowledge chains, so that we can comprehend human sense and its problem-solving method from a higher level. In search of the models or methods to perform those theories fusion with methods, the dissertation investigates synthesis reasoning technology mainly combined with CBR and RS from many aspects. The findings indicate that effective synthesis reasoning contributes to an essential knowledge repository vital for incomplete knowledge discovery to make informed strategic decisions.
According to the real problem-solving demands, synthesis reasoning can combine various reasoning principles and integrate many methods; consequently it can apply many kinds of knowledge from various levels, and enhance the system's efficiency of reasoning by means of adaptable knowledge granularity. Gradually it shall tend to set up a universal granular computing platform, which can deal with complex and fuzzy information more efficient, and provide reliable techniques in construction case intelligent systems, enlarging the system's ability of problem-solving, a framework of case-intelligent DSS is therefore figured out.
Thirdly, the dissertation studies intelligent case retrieval techniques. After investigating the behavior of MFNN together with many kinds of existent algorithms for case retrieval based on MFNN, besides widely used back propagation algorithm (BP), simulated annealing algorithm and their ameliorated algorithms, it points out that weaknesses such as having lower speed and local extreme value, are inherent in those algorithms, thus cannot be conquered thoroughly. Compared with those former algorithms, radial basis function network (RBF), which recognizes and classifies samples dependence on their non-linear distance through projecting them to a multi dimensional space, is a good similarity detector. The subsequent research indicates that RBF network has so many advantages like fast learning and global convergence, then the dissertation puts forward a model for similar case retrieval based on RBF network.
Fourthly, the dissertation investigates constructive neural networks together with their learning methods, and masters the learning course of neural networks from overall viewpoint. Any algorithm shall engage with certain characters of the neural network, which is selected as one of the purpose and should be taken care of. It is a significant leap to study constructive neural networks, which is capable of large-scale problem solving. The study begins with the MP neuron model from the view of geometry, and traces into covering algorithm (CA) from forward propagation algorithm (FP, compared with BP), the former algorithm of constructive neural networks. The sequent investigations indicate that CA has quite a number of characters, for example, clear system structure, feasible component for combination, running fast company with high recognizing ratio, etc., and it can be easily integrated and constructed. Then, the dissertation puts forward a model-case intelligent system-based on constructive neural networks and covering algorithm.
Finally, the dissertation investigates case knowledge base maintenance and puts forward some criteria for this cycle. The main knowledge base of the CBR system is the case library, and its learning ability is continuously transacting adding new cases, just as people accumulate their knowledge. The knowledge implicit in case library is involved in CBR process, such as case representation, case matching and case adaptation, thus case base maintenance (CBM) becomes one of the key problem in CBR research. As an important branch of the CBR community, CBM has developed some kinds of methods, but they should be restricted to their running conditions, such as CBR system's scale, aging effect even the working area, and CBM methods must be adapted for any changes.
Considering redundant cases or inconsistent knowledge possibly caused by the changing environment, the dissertation puts forward a CBM strategy based on Similarity Rough Set technique, which can achieve real-time maintenance, and implement a controllable threshold according to the selected granularity as a real-time monitor.
Aiming to the efficiency of case retrieval in the irreducible case library, for that CBR systems running in interactive domains like e-commerce, online helpdesk, fault diagnosis etc., and can easily reach thousands of cases, the dissertation puts forward another CBM strategy based on covering algorithm and MFNN to achieve CBM from both sides: One is employing Alternative-Covering Algorithm to partition the case library to many Covering Domains and thus realizing the selective filtering; the other is using MFNN to deal with case retrieval within the large-scale case library. Our experimental results indicate that the integrated method, which is especially feasible for the processing of vast, multiclass and high dimensional data, can effectively guarantee the system's usability and enhance its capability.
引文
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