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.     

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。     

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     

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.     

An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks

The aim of this work is to classify the sections of coils produced on a cool rolling mill that have an irregular thickness pattern, in order to achieve a homogeneous thickness in each coil. In order to do this investigation, we have employed a self-organising map (SOM) of neural networks, a new segmentation and clustering algorithm, filters to reduce the noise and, finally, a classification calculated from the difference between the value of each sample taken and the average of them all. We have introduced an alternative approach, with improvements in the segmentation and clustering steps, which has been successfully applied in an industrial production line. Some of our limitations and future areas for investigation are also included.     

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.     

Retrieving information on the World Wide Web: effects of domain specific knowledge

In this study, we intend to examine information retrieval behaviors from a psychological point of view using a search engine on the World Wide Web (WWW). We investigated information retrieving behaviors in detail based on both the recorded data of retrievers’ web browsing actions and their thinking processes by the “think aloud” method. We focused on selected keywords for retrieving and compared them between retrievers who had enough knowledge about their task and those who did not. Our goal was to learn about the literacy needed for finding required information efficiently on the WWW.

Self-organizing neural networks for the analysis and representation of data: Some financial cases

Many recent papers have dealt with the application of feedforward neural networks in financial data processing. This powerful neural model can implement very complex nonlinear mappings, but when outputs are not available or clustering of patterns is required, the use of unsupervised models such as self-organizing maps is more suitable. The present work shows the capabilities of self-organizing feature maps for the analysis and representation of financial data and for aid in financial decision-making. For this purpose, we analyse the Spanish banking crisis of 1977–1985 and the Spanish economic situation in 1990 and 1991, making use of this unsupervised model. Emphasis is placed on the analysis of the synaptic weights, fundamental for delimiting regions on the map, such as bankrupt or solvent regions, where similar companies are clustered. The time evolution of the companies and other important conclusions can be drawn from the resulting maps.Characters and symbols used and their meaning nx x dimension of the neuron grid, in number of neurons - ny y dimension of the neuron grid, in number of neurons - n dimension of the input vector, number of input variables - (i, j) indices of a neuron on the map - k index of the input variables - w _ijk synaptic weight that connects thek input with the (i, j) neuron on the map - W _ij weight vector of the (i, j) neuron - x _k input vector - X input vector - (t) learning rate - _o starting learning rate - _f final learning rate - R(t) neighbourhood radius - R₀ starting neighbourhood radius - R _f final neighbourhood radius - t iteration counter - t _rf number of iterations until reachingR _f - t _f number of iterations until reaching _f - h(·) lateral interaction function - standard deviation - for every - d (x, y) distance between the vectors x and y     

Study on optimal design of pressure-adjusting spring using artificial neural networks

A preliminary discussion has been carried out on the traditional optimization design method for pressure-adjusting spring of relief valve. Based on the traditional optimization methods about the pressure-adjusting spring of the relief valve and combined with the advantages of neural network, this paper puts forward the optimization method with many parameters and a lot of constraints based on neural network in order to find the maximal inherent frequency. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure-adjusting spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure-adjusting spring. The specified technical performances of the relief valve are certified by experiments. The results of experiments showed that by configuring pressure-adjusting spring the dynamic performance and working stability of the relief valve are enhanced.     

Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.     

Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach

The paper focuses on methods for injecting prior knowledge into adaptive recurrent networks for sequence processing. In order to increase the flexibility needed for specifying partially known rules, a nondeterministic approach for modelling domain knowledge is proposed. The algorithms presented in the paper allow time-warping nondeterministic automata to be mapped into recurrent architectures with first-order connections. These kinds of automata are suitable for modeling temporal scale distortions in data such as acoustic sequences occurring in problems of speech recognition. The algorithms output a recurrent architecture and a feasible region in the connection weight space. It is demonstrated that, as long as the weights are constrained into the feasible region, the nondeterministic rules introduced using prior knowledge are not destroyed by learning. The paper focuses primarily on architectural issues, but the proposed method allows the connection weights to be subsequently tuned to adapt the behavior of the network to data.     

Simulation of heterogeneous neural networks on serial and parallel machines

There has recently been a tremendous rebirth of interest in neural networks, ranging from distributed and localist spreading-activation networks to semantic networks with symbolic marker-passing. Ideally these networks would be encoded in dedicated massively-parallel hardware that directly implements their functionality. Cost and flexibility concerns, however, necessitate the use of general-purpose machines the simulate neural networks, especially in the research stages in which various models are being explored and tested. Issues of a simulation's timing and control become more critical when models are made up of heterogeneous networks in which nodes have different processing characteristics and cycling rates or which are made up of modular, interacting sub-networks. We have developed a simulation environment to create, operate, and control these types of connectionist networks. This paper describes how massively-parallel heterogeneous networks are simulated on serial machines as efficiently as possible, how large-scale simulations could be handled on current SIMD parallel machines, and outlines how the simulator could be implemented on its ideal hardware, a large-scale MIMD parallel machine.     

Implementations of artificial neural networks on the Connection Machine

A review of five distinct artificial neural network implementations on the Connection Machine is presented along with a brief discussion of the more general issues surrounding the implementation of artificial neural network models in parallel. The implementation which proves to be fastest on the Connection Machine is parallel in the training patterns and runs at more than 1300 million interconnects per second.     

深度神经网络模型构建及优化策略

针对深度学习构建网络模型以及确定模型参数的问题,在分析神经网络基本结构和线性模型局限性的基础上,研究了深度神经网络设计的关键因素和优化策略。结合手写数字识别问题,对优化策略、动态衰减学习率、隐藏层节点数、隐藏层数等情形下的识别正确率进行了实验。结果表明,不同神经网络模型对最终正确率有质的影响,相同优化策略在不同参数取值时对最终正确率有很大影响,并进一步探究了具体选取优化策略和参数的方法。