A metagenetic algorithm for information filtering and collection from the World Wide Web

This paper describes the implementation of evolutionary techniques for information filtering and collection from the World Wide Web. We consider the problem of building intelligent agents to facilitate a person's search for information on the Web. An intelligent agent has been developed that uses a metagenetic algorithm in order to collect and recommend Web pages that will be interesting to the user. The user's feedback on the agent's recommendations drives the learning process to adapt the user's profile with his/her interests. The software agent utilizes the metagenetic algorithm to explore the search space of user interests. Experimental results are presented in order to demonstrate the suitability of the metagenetic algorithm's approach on the Web.     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets

This research develops a methodology for the intelligent remote monitoring and diagnosis of manufacturing processes. A back propagation neural network monitors a manufacturing process and identifies faulty quality categories of the products being produced. For diagnosis of the process, rough set is used to extract the causal relationship between manufacturing parameters and product quality measures. Therefore, an integration of neural networks and a rough set approach not only provides information about what is expected to happen, but also reveals why this has occurred and how to recover from the abnormal condition with specific guidelines on process parameter settings. The methodology is successfully implemented in an Ethernet network environment with sensors and PLC connected to the manufacturing processes and control computers. In an application to a manufacturing system that makes conveyor belts, the back propagation neural network accurately classified quality faults, such as wrinkles and uneven thickness. The rough set also determined the causal relationships between manufacturing parameters, e.g., process temperature, and output quality measures. In addition, rough set provided operating guidelines on specific settings of process parameters to the operators to correct the detected quality problems. The successful implementation of the developed methodology also lays a solid foundation for the development of Internet-based e-manufacturing.     

网络信息搜寻技术与发展

信息搜寻是网络技术的新课题。本文介绍了在因特网上进行信息搜寻采用的算法、策略和现有检索工具的工作原理以及网络信息搜寻技术的新发展。     

Distributed and cooperative information retrieval on the World Wide Web

A masss of heterogeneous,distributed and dynamic information on the World Wide Web(the Web) has resulted in “information overload“ .It‘s an important and urgent reserach issue to provide users with effective information retrieval service on the Web.Web search enginees attempt to solve this problem,yet their effect is far from satisfying.In this paper,a distributed and cooperative strategy for information retrieval on the Web is proposed to substitute the centralized mode adopted by the current search engines.Then a new information retrieval system model IRSM is presented.which supports the retrieval of metadata about web documents and uses Z39.50 standard protocol to unify the heterogeneous interfaces of uments and uses Z39.50 standard protocol to unify the heterogeneous interfaces of different systems.Based on that,a distributed and cooperative information refieval framework,called DCIRF,is designed to help users in fast and effective information retrieval on the Web.     

Integrated optimal topology design and shape optimization using neural networks

In this paper, neural network- and feature-based approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization. The topology optimization results are reconstructed in terms of features, which consist of attributes required for automation and integration in subsequent applications. Features are defined as cost-efficient simple shapes for manufacturing. A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features. The effectiveness of the proposed approach in integrating topology design and shape optimization is demonstrated with several experimental examples.     

Non-linear variable selection for artificial neural networks using partial mutual information

Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. However, the selection of an appropriate set of input variables during ANN development is important for obtaining high-quality models. This can be a difficult task when considering that many input variable selection (IVS) techniques fail to perform adequately due to an underlying assumption of linearity, or due to redundancy within the available data.This paper focuses on a recently proposed IVS algorithm, based on estimation of partial mutual information (PMI), which can overcome both of these issues and is considered highly suited to the development of ANN models. In particular, this paper addresses the computational efficiency and accuracy of the algorithm via the formulation and evaluation of alternative techniques for determining the significance of PMI values estimated during selection. Furthermore, this paper presents a rigorous assessment of the PMI-based algorithm and clearly demonstrates the superior performance of this non-linear IVS technique in comparison to linear correlation-based techniques.     

基于RBF网络的信息融合在机器人足球中的应用

机器人足球系统是综合性的人工智能研究平台。决策在机器人足球比赛中起着至关重要的作用。通过对机器人足球系统的分析,论证了信息融合应用于机器人足球系统的可行性。针对机器人足球比赛决策中的实际问题,提出了基于径向基函数(RBF)神经网络的信息融合方法,并设计了足球机器人射门实验。实验结果证明该方法有助于提高整个系统决策的准确性。     

模糊神经网络信息融合在管道机器人导航中的应用

考虑到石油管道的封闭性和复杂性,很难识别环境特征,将基于模糊神经网络的多传感器信息融合用于解决管道中管道机器人的导航问题。采用CCD摄像头和距离传感器来识别管道中的障碍物和弯道,并根据环境信息制定控制决策。建立了机器人物理模型和模糊神经网络拓扑结构,并对神经网络进行了学习训练。最后,对其中一种环境类型进行了仿真验证,证实了算法的有效性。     

模糊神经网络在移动机器人信息融合中的应用

针对移动机器人所用的传感器,提出了一种用于多传感器信息融合的方法,将模糊逻辑和神经网络结合起来,构建了模糊神经网络,并建立了网络的计算模型.通过建立的模糊神经网络对移动机器人的多传感器信息进行融合,实现了移动机器人对动态环境中障碍和环境类型的实时识别以及无冲突运动.网络的训练和试验表明该方法在移动机器人躲避运动物体中是可行的.     

过程神经元网络及其在时变信息处理中的应用

针对时变信息处理和动态系统建模等类问题,建立了输入输出均为时变函数的过程神经元网络和有理式过程神经元网络2种网络模型.在输入输出为时变函数的过程神经元网络中,过程神经元的时间累积算子取为对时间的积分或其他代数运算,它的时空聚合机制和激励能同时反映外部时变输入信号对输出结果的空间聚合作用和时间累积效应,可实现非线性系统输入、输出之间的复杂映射关系.在有理式过程神经元网络中,其基本信息处理单元为由2个成对偶出现的过程神经元组成,逻辑上分为分子和分母2部分,通过有理式整合后输出,可有效提高过程神经元网络对带有奇异值过程函数的柔韧逼近性和在奇异值点附近反应的灵敏性.分析了2种过程神经元网络模型的性质,给出了具体学习算法,并以油田开发过程模拟和旋转机械故障诊断问题为例,验证了这2种网络模型在时变信息处理中的有效性.     

A hybrid approach to vehicle routing using neural networks and genetic algorithms

A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are distributed over the entire geographic area during the initialization phase, while the genetic algorithm finds good parameter settings in the route construction phase that follows. Computational results on a standard set of problems are also reported.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of     

Feasibility of probabilistic neural networks, Kohonen self-organizing maps and fuzzy clustering for source localization of ventricular focal arrhythmias from intravenous catheter measurements

Abstract: An important goal of clinical electrophysiological studies is estimation of the source of rhythm disturbances (arrhythmia) in the heart. 15% of ventricular arrhythmias are known to originate from the outer surface of the heart (epicardium). One localization approach targeting the epicardium uses multielectrode catheters placed in the coronary veins. However, epicardial measurement sites from these catheters are limited to locations reached via the coronary veins. This study investigates the feasibility of several pattern classification and neural network approaches for localization of the source of ventricular arrhythmias from sparse measurements acquired from within coronary veins. Specifically, we studied Kohonen self-organizing maps and fuzzy C-means clustering methods for the construction of the target vector in neural networks from experimental high-resolution activation-time patterns. We also studied two neural network techniques, probabilistic neural networks and backpropagation networks, for the training and test procedures. The results of this study showed that it was possible to localize the arrhythmia source in a relatively small region for approximately 70% of cases. This study, in general, showed that the combination of probabilistic neural networks, Kohonen self-organizing maps and fuzzy C-means clustering approaches is feasible in catheter-based epicardial arrhythmia source localization.     

Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise.     

A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.     

Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points

Fuzzy cognitive mapping is commonly used as a participatory modelling technique whereby stakeholders create a semi-quantitative model of a system of interest. This model is often turned into an iterative map, which should (ideally) have a unique stable fixed point. Several methods of doing this have been used in the literature but little attention has been paid to differences in output such different approaches produce, or whether there is indeed a unique stable fixed point. In this paper, we seek to highlight and address some of these issues. In particular we state conditions under which the ordering of the variables at stable fixed points of the linear fuzzy cognitive map (iterated to) is unique. Also, we state a condition (and an explicit bound on a parameter) under which a sigmoidal fuzzy cognitive map is guaranteed to have a unique fixed point, which is stable. These generic results suggest ways to refine the methodology of fuzzy cognitive mapping. We highlight how they were used in an ongoing case study of the shift towards a bio-based economy in the Humber region of the UK.     

异质网中基于图卷积神经网络的链路预测方法

综合考虑异质信息网络具有的复杂性和异质性的特点,提出一种异质网中基于图卷积神经网络(heterogeneous graph convolution neural network embedding,HeGCNE)的链路预测方法.针对经典图卷积神经网络逐层传递规则的不足,提出改进的逐层传递规则,对异质节点进行表征学习,融...     

非线性系统神经网络自适应控制的发展现状及展望

简要回顾了神经网络控制及其应用的发展历程,重点论述了人们在连续、离散时间非线性系统的神经网络以及神经模糊稳定自适应控制研究方面所取得的主要进展,探讨了神经网络自适应控制研究方面存在的主要问题及解决问题的基本途径.作为当前解决神经网络自适应控制问题的途径之一,介绍了近来人们对二阶模糊神经网络以及量子神经网络的研究.最后,总结并指出了这一领域下一步的发展方向和有待解决的新课题.     

Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach

A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability.