A transaction pattern analysis system based on neural network

Customer segmentation is a key element for target marketing or market segmentation. Although there are quite a lot of ways available for segmentation today, most of them emphasize numeric calculation instead of commercial goals. In this study, we propose an improved segmentation method called transaction pattern based customer segmentation with neural network (TPCSNN) based on customer’s historical transaction patterns. First of all, it filters transaction data from database for records with typical patterns. Next, it reduces inter-group correlation coefficient and increases inner cluster density to achieve customer segmentation by iterative calculation. Then, it utilizes neural network to dig patterns of consumptive behaviors. The results can be used to segment new customers. By this way, customer segmentation can be implemented in very short time and costs little. Furthermore, the results of segmentation are also analyzed and explained in this study.     

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.     

A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.     

基于模糊贝叶斯网络的堆垛机系统可靠性分析

为解决因缺乏实际数据而无法准确估计堆垛机系统和部件的失效概率问题,提出了基于模糊集理论和主观贝叶斯方法的模糊贝叶斯网络诊断策略.该方法首先将故障树转换成相应的贝叶斯网络,然后运用模糊集理论,将专家给出的关于基本事件失效概率的主观语言评判值转换成模糊数,并通过去模糊化处理得到精确解.针对因事件的多态性所引起的条件概率不确定问题,该方法采用主观贝叶斯方法进行估计.通过堆垛机通信模块的可靠性分析实例,验证了该方法是有效的,表明其能够克服在系统建模时的参数不确定问题.     

A multi-agent system to construct production orders by employing an expert system and a neural network

The authors describe the implementation of a multi-agent system, whose goal is to enhance production planning i.e. to improve the construction of production orders. This task has been carried out traditionally by the module known as production activity control (PAC). However, classic PAC systems lack adaptive techniques and intelligent behaviour. As a result they are mostly unfit to handle the NP Hard combinatorial problem underlying the construction of right production orders. To overcome this situation, we illustrate how an intelligent and collaborative multi-agent system (MAS) obtains a correct production order by coordinating two different techniques to emulate intelligence. One technique is performed by a feed-forward neural network (FANN), which is embedded in a machine agent, the objective being to determine the appropriate machine in order to fulfil clients’ requirements. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The entire MAS consists of a coordinator, a spy, and a scheduler. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. The scheduler agent programs the production orders. We achieve a realistic MAS that fully automates the construction and dispatch of valid production orders in a factory dedicated to produce labels.     

基于神经网络的模糊史密斯控制系统

本文提出了一种利用BP神经网络进行在线自整定的模糊史密斯预估控制系统,该系统结合了BP神经网络、模糊控制、史密斯预估控制的优点,通过仿真实验表明,这种控制方法对具有大滞后性,时变性,干扰因素不确定性的系统有较好的控制效果.     

A robotic system based on neural network controllers

In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control (such as the computed torque technique) require high computational time and can result in a poor control performance, if the specific model-structure selected does not properly reflect all the dynamics. The control technique proposed here has provided satisfactory results. A decentralised model has been assumed here where a controller is associated with each joint and a separate neural network is used to adjust the parameters of each controller. Neural networks have been used to adjust the parameters of the controllers, being the outputs of the neural networks, the control parameters.     

基于神经网络逼近的异构多智能体系统二分拟一致性

研究一类具有未知动力学的二阶异构非线性多智能体系统二分拟一致性问题.针对二阶多智能体系统中未知的非线性动态,基于神经网络逼近理论设计一类自适应控制协议,以保证所有智能体最终收敛到有界区域内.借助Lyapunov稳定性理论和不等式技巧得到异构多智能体系统实现领导-跟随二分拟一致性的充分性条件,并给出一致性误差的上界.最后通过数值仿真验证了理论结果的有效性.     

融合上下文字符信息的泰语神经网络分词方法

自动分词是自然语言处理的关键基础技术。针对传统泰语统计分词方法特征模板复杂、搜索空间大的问题,提出融合上下文字符信息的泰语神经网络分词模型。该模型借助词分布表示方法,训练泰语字符表示向量,利用多层神经网络分类器实现泰语分词。基于InterBEST 2009泰语分词评测语料的实验结果表明,所提方法相较于条件随机场分词模型、Character-Cluster Hybrid 分词模型以及 GLR and N-gram 分词模型取得了更好的分词效果,分词准确率、召回率和F值分别达到了97.27%、99.26 %及98.26 %,相比条件随机场分词速度提高了112.78%。     

一种基于用户情境的QoS预测方法

完整的QoS信息有利于更准确的服务推荐,但是现实中往往很难得到。文章提出了一种基于用户情境的QoS预测方法,对于老用户,根据他们原来的QoS选择,考虑QoS类型区别和时间衰减情况,预测新的QoS取值;对于新用户,按照用户分类信息,根据同类用户的服务选择情况,预测他们的QoS取值。实验证明,该方法有助于提高服务推荐的性能。     

Resource collaboration system based on dynamic user preference and context

In a ubiquitous computing environment, service composition and collaboration among heterogeneous resources are required, thus, an infrastructure that supports these requirements is an essential factor in seamless service delivery. In this environment, users hope to get a variety of customized services by using only an individual mobile device. But the resource of the mobile device has limitations such as tiny display screens, limited input, less powerful processors, and limited storage. Moreover each user situation is different and user preferences are also various. Therefore it is one of new issues to provide a customized service for a user through resource collaboration based on various user preference and situation. To solve this issue, this paper proposes a resource collaboration system which infers customized resources for composing a user required service and collaborative with selected resources. For our collaboration system, this paper proposes the method to infer resources based on the context and user preferences including dynamic change of the preference. This paper also shows a reasonable execution environment for the proposed system through the performance evaluation in server-client and peer-to-peer environments.     

一种基于神经网络和贝叶斯决策的人脸检测方法

提出了一种利用BP神经网络仿真、利用贝叶斯决策修正仿真结果的人脸检测方法.讨论了单纯使用BP神经网络作人脸的检测判定的不足,并在此基础上提出利用贝叶斯决策对神经网络的仿真结果进行第二次判定的方法.共使用MITEx人脸库的4 000个人脸与非人脸图像进行实验分析,正确率平均提升了3.63%,表明了神经网络的良好判定性能和使用贝叶斯决策进行修正的有效性和必要性.     

A user authentication system using back-propagation network

Information security has been a critical issue in the field of information systems. One of the key factors in the security of a computer system is how to identify the authorization of users. Password-based user authentication is widely used to authenticate a legitimate user in the current system. In conventional password-based user authentication schemes, a system has to maintain a password table or verification table which stores the information of users IDs and passwords. Although the one-way hash functions and encryption algorithms are applied to prevent the passwords from being disclosed, the password table or verification table is still vulnerable. In order to solve this problem, in this paper, we apply the technique of back-propagation network instead of the functions of the password table and verification table. Our proposed scheme is useful in solving the security problems that occurred in systems using the password table and verification table. Furthermore, our scheme also allows each user to select a username and password of his/her choice.     

基于分布式智能代理的入侵检测方法研究

在分析和研究通用入侵检测框架理论和传统入侵检测系统实现策略的基础上,提出融合了滥用检测和异常检测两种方法的检测模型——基于分布式智能代理的网络入侵检测模型,并对检测引擎和检测算法进行了改进,使之具有更高的准确性和对潜在的入侵行为的识别和预测等智能化能力。     

A user model neural network for a personal news service

User modelling has been widely applied to pedantic situations, where we are attempting to infer the user's knowledge. In teaching it is important to know that the user has mastered the elementary concepts before proceeding with the advanced topics. However, the application of user modelling to information retrieval demands a quite different type of user model. Here we construct a user model for browsing, where the user is uncertain of exactly which information he desires. This requires a more inexact and robust user model, that can quickly give guidance to the system. We propose a user model based on neural networks that can be constructed incrementally. Performance of the model shows some promise for this approach. We discuss the advantages and limitations of the approach and its implications for user modelling.     

非仿射多智能体系统的自适应神经网络控制

针对有向拓扑图下一类控制方向未知的非仿射非线性多智能体系统的输出一致性问题,综合运用中值定理、RBF神经网络及其特性、Nussbaum增益函数方法和动态面控制技巧,提出一种分布式自适应神经网络控制协议,保证跟随者的输出能与领导者的输出同步,跟踪误差能保持在零点的小邻域内.采用新的非线性滤波器代替传统动态面控制方法(CD...     

基于贝叶斯网络的自适应系统建构

以认知学习理论为依据,运用贝叶斯网络建立学习者模型,提出了一种学习内容自适应呈现算法。学习内容的呈现适合学习者认知发展水平及个性特征,实现了一种智能化、个性化网络学习的自适应系统,为学习者提供一种更优化的学习途径。     

A neural network approach to a Bayesian statistical decisionproblem

Generalized mean-squared error (GMSE) objective functions are proposed that can be used in neural networks to yield a Bayes optimal solution to a statistical decision problem characterized by a generic loss function.     

一种基于BP神经网络的多传感器系统信号降噪算法

传感器在对目标检测时,输出信号不可避免地含有白噪声.利用BP神经网络的非线性映射能力,提出一种基于BP网络的多传感器系统信号降噪压缩方法.多传感器含噪声的输出信号序列和目标真值作为样本,用于网络训练,用检验样本对训练后的网络进行检验,并与最优加权以及最优加权与递推最小二乘法相结合的滤波方法比较.MATLAB下的仿真结果表明:BP网络用于多传感器系统滤波有明显效果.     

基于可微神经计算机和贝叶斯网络的知识推理方法

针对人工神经网络(ANN)对面向知识图谱(KG)的知识推理的记忆能力有限以及KG无法处理不确定知识的问题,提出一种可微神经计算机(DNC)和贝叶斯网络(BN)相结合的推理方法DNC-BN.首先,将长短时记忆(LSTM)网络作为控制器,在每个时刻对输入向量和从记忆体获取的读向量进行处理,得到网络输出向量和交互向量;其次,...