符号网络分析系统

本文介绍了作者用符号演算方法，在微型计算机上实现的一套符号网络分析系统。使用这套系统的作用络辅助分析与设计，可得精确的符号解，还可清地了解每个元件在网络中的作用和贡献。     

一个混合型系统在天气预报中的应用

本文描述了一个在天气预报领域工作的混合系统，它将符号处理与神经网络有机合成，利用符号处理系统作神经网络的前端。而神经网络又被分为4个子网络，各个子网络间可相互作用，文中提出了一个简单的方法以确定网络的隐层结点数。     

基于神经网络的符号自动识别系统研究

提出了一种新的基于神经网络的目标符号自动识别系统。该系统在图像的二值化处理过程，采用了小波变换的方法，该方法可有效克服噪声的干扰，自动确定灰度图像二值化所需要的阈值。在符号识别部分，采用了双向联想记忆（BAM）人工神经网络技术，通过改进的感知器学习算法，增大了网络的容量，可实现对采集的有污染或缺损符号进行正确识别。仿真实验结果说明，系统具有较强的稳定性和有效性，且易于工程实现。     

有向符号网络的边能控性研究

符号网络是一类具有正负符号特征的网络.在多智能体系统中,符号网络能够描述智能体之间的合作与对抗交互关系,因此受到学者的广泛关注.本文主要研究有向符号网络的边能控性.首先,对具有符号网络的多智能体系统边动力学进行建模,得到边能控性模型.其次,从网络拓扑结构角度对边能控子空间进行定量刻画,利用有向符号网络的距离和等价划分得到能控子空间的上下界估计.进一步,讨论了符号网络边能控性与顶点能控性的关系.所得结果表明:当顶点符号图为结构非平衡时,符号网络的边能控性与顶点能控性等价.最后,通过仿真结果验证所得理论的有效性.     

基于模糊描述逻辑的PNL网络问答系统

将现有网络搜索引擎过渡到网络问答系统是WI的基本目标之一，模糊问答系统是网络问答系统研究的重要问题。基于模糊描述逻辑，该文特化了PNL过程，提出了PNL式模糊网络问答系统。该系统将模糊描述逻辑规则作为PNL 推理过程中用到的原型语言规则中的计算部分，推理出原型语言规则中的知识符号，并以一个具体的问答实例，介绍了PNL式问答系统的工作流程。     

使用UML分析设计嵌入式系统

标准建模语言UML是最广泛使用的可视化面向对象系统的建模方法。介绍了使用U ML对嵌入式系统—网络收音机作面向对象软件分析与设计的过程。网络收音机是一款收听 网上广播电台节目的信息家电产品。论述了利用UML的各种标准符号进行从需求分析开 始，到系统整体设计的一系列工作。     

混合型多概念获取系统的设计与实现

本文主要描述了一个增量式混合型多概念获取系统ＨＭＣＡＳ，它提出了一个基于概率论的符号学习与神经网络学习相结合的学习算法，能从隶属于某个概念集的实例集中归纳出满足用户精度要求的，以浊合型判定树表示的概念描述。在ＨＭＣＡＳ中，符号学习与神经网络学习具有结合紧密的转换灵活等特点，具有较高的学习效率和较强的归纳能力以及增量学习能力。ＨＭＣＡＳ的神经网络学习可选择ＢＰ网络或ＦＴＡＲＴ网络，其推理机制提供了混     

卷积信道编码与物理层网络编码的联合设计

针对无人机作为中继平台在异步通信条件下数据传输误码率高的问题,提出将物理层网络编码与卷积信道编码联合设计的方案。在方案中,中继节点引入置信传播(Belief Propagation)算法,将信道译码和网络编码联合起来,解决了符号偏移和相位偏移对通信系统的影响。仿真结果显示,该方案与未进行信道编码和RA码信道编码方式相比误码率最低,提高了系统的鲁棒性,可以更好地抵制相位偏移和符号偏移对通信系统的影响,提高无人机的抗干扰能力。     

基于MapX复杂符号标绘的一种新方法

在一些需要使用复杂符号甚至动态符号基于MapX的GIS系统里，Mapx提供的符号标绘功能存在明显的不足，不能满足一些系统的实际需要。鉴于此，本文基于对MapX的内部实现机制的分析，提出一种新的实现复杂符号标绘的技术方法，同时给出了在VC＋＋环境下的具体实现过程。实验证明，该方法可以使我们在MapX上标绘任意复杂的动静态符号。     

符号网络链接预测算法研究综述

符号网络链接预测包括网络结构上两个节点间未知链接的可能性预测与符号预测两方面,其相关研究对于分析和理解符号网络的拓扑结构、功能及演化行为具有十分重要的意义,在个性化推荐、态度预测、蛋白质交互作用研究等领域有着重大的应用价值。文中综述了符号网络链接预测问题的研究成果,介绍了相关概念、符号网络的理论基础、常用符号网络数据集以及预测精度评价标准;将目前主要的符号网络链接预测算法按照设计思路分为有监督学习与无监督学习两大类,详细阐述了每种算法的主要思想;归纳总结了符号网络链接预测问题的特点和规律,讨论了目前存在的问题并指出了面临的挑战和未来可能的发展方向。这能为信息学、生物学、社会学等领域的相关研究人员提供有益参考。     

Combining Symbolic and Neural Learning

Conclusion Connectionist machine learning has proven to be a fruitful approach, and it makes sense to investigate systems that combine the strengths of the symbolic and connectionist approaches to AI. Over the past few years, researchers have successfully developed a number of such systems. This article summarizes one view of this endeavor, a framework that encompasses the approaches of several different research groups. This framework (see Figure 1) views the combination of symbolic and neural learning as a three-stage process: (1) the insertion of symbolic information into a neural network, thereby (partially) determining the topology and initial weight settings of a network, (2) the refinement of this network using a numeric optimization method such as backpropagation, possibly under the guidance of symbolic knowledge, and (3) the extraction of symbolic rules that accurately represent the knowledge contained in a trained network. These three components form an appealing, complete picture—approximately-correct symbolic information in, more-accurate symbolic information out—however, these three stages can be independently studied. In conclusion, the research summarized in this paper demonstrates that combining symbolic and connectionist methods is a promising approach to machine learning.     

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.     

SYMCON—A Hybrid Symbolic/Connectionist System for Word Sense Disambiguation

Connectionist methods and knowledge-based techniques are two largely complementary approaches to natural language processing (NLP). However, they both have some potential problems which preclude their being a general purpose processing method. Research reveals that a hybrid processing approach that combines connectionist with symbolic techniques may be able to use the strengths of one processing paradigm to address the weakness of the other one. Hence, a system that effectively combines the two different approaches can be superior to either one in isolation. This paper describes a hybrid system—SYMCON (SYMbolic and CONnectionist) which integrates symbolic and connectionist techniques in an attempt to solve the problem of word sense disambiguation (WSD), which is arguably one of the most fundamental and difficult issues in NLP. It consists of three sub-systems: first, a distributed simple recurrent network (SRN) is trained by using the standard back-propagation algorithm to learn the semantic relationships among concepts, thereby generating categorical constraints that are supplied to the other two sub-systems as the initial results of pre-processing. The second sub-system of SYMCON is a knowledge-based symbolic component consisting of a knowledge base containing general inferencing rules in a certain application domain. Third, a localist network is used to select the best interpretation among multiple alternatives and potentially ambiguous inference paths by spreading activation throughout the network. The structure, initial states, and connection weights of the network are determined by the processing outcome in the other two sub-systems. This localist network can be viewed as a medium between the distributed network and the symbolic sub-system. Such a hybrid symbolic/connectionist system combines information from all three sources to select the most plausible interpretation for ambiguous words.     

Effective data mining using neural networks

Classification is one of the data mining problems receiving great attention recently in the database community. The paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems     

代数符号计算的神经网络模型

文中给出一种p-adic数制式非对称连接神经网络模型,该网络在整个矢量空间只有唯一平衡点,因而可获得问题的最优解,且在存在计算误差,这种神经网络保持高度并行结构,可用了代数符号计算,本文重点分析了实现神经网络的方法,给代数符号计算提供了一个新的计算模型。     

热力学数据库符号运算系统研究与实现

为了解决热力学数据库符号运算问题,本文分析Matlab混合编程方法,构建基于Matlab引擎和Web Service的热力学数据库符号运算系统,简化热力学数据库实现符号运算的过程,同时也实现异构热力学数据库与热力学数据库符号运算系统的集成。     

High-Order Two-Dimension Cluster Competitive Activation Mechanisms Used for Performing Symbolic Logic Algorithms of Problem Solving

This paper presents a neural network approach,based on high-order twodimension temporal and dynamically clustering competitive activation mechanisms,to implement parallel searching algorithm and many other symbolic logic algorithms.This approach is superior in many respects to both the common sequential algorithms of symbolic logic and the common neural network used for optimization problems.Simulations of problem solving examples prove the effectiveness of the approach.     

A Java framework for massively distributed symbolic computing

We present a framework for implementing massively distributed applications in symbolic computing. Using this framework, computations with massive resource requirements can be distributed and processed in parallel on a network of workstations or on a large scale network such as the Internet. For each concrete application only minimal code is needed to complement the generic framework in order to enable large scale distributed processing of the application.Our framework introduces a “divide and be conquered” model for massively distributed computations. We compare this model to a more traditional one, in a symbolic computing setting. We stress the major problems and propose solutions for some of them.