Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network

In this paper, an intelligent operation system, which consists of an intelligent diagnostic subsystem (with a neural network) and an intelligent maintenance subsystem (with an expert system), was presented and discussed. The artificial neural network and the expert system, which use the information developed in the neural network, perform a special function in this system. The functional combination of the artificial neural network and the expert system together created a new solution in the form of an intelligent system, which was referred to as an intelligent maintenance system. This article also covers decision-making methods that are used in an expert maintenance system and whose purpose is an organization and control of the process of the prevention of technical objects. For this purpose, the method was described of taking decisions by an expert for complex parametric type hypotheses and for simple finished type hypotheses in the set of possible decisions’ hypotheses. A considerable part of this paper covers the presentation of the method to transform diagnostic information into the required form of maintenance information. For this purpose, an algorithm of the work of maintenance system was performed and descried. In the creation process of the maintenance knowledge base, the specialist knowledge of a human specialist was also used. Hence, a skilful and proper taking of decisions by an expert to create this set of information is essential. Two inference methods were characterized and described in this paper. The theoretical results obtained were verified in the examination of the influence of each of these decision-making inference methods on the final results of the process of the prevention treatment of an object.     

Investigation of the operation process of a repairable technical object in an expert servicing system with an artificial neural network

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object.     

Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object.     

基于规则的核电站故障诊断神经网络系统研制

人工神经网络方法越来越多地用于核电站诊断系统中,但是由于神经网络训练需要大量的训练样本并且诊断过程的不透明性,使得人工神经网络的应用受到限制。论文提出了一种人工神经网络故障诊断系统,结合了人工神经网络、模糊控制技术和专家系统的优点,使诊断过程易于理解,而且能够获得相应的解释,有更大的适应性。     

蜜蜂群优化算法用于训练前馈神经网络

训练人工神经网络的目的是调整各层的权重系数以达到最优,因而训练过程的实质是一项优化任务。传统的训练算法存在着容易陷入局部最优、计算复杂等缺陷。介绍一种训练前馈神经网络的蜜蜂群优化算法,它是一种简单、鲁棒性强的群体智能随机优化算法。该算法把探查和开发过程有效地结合在一起,并采取了跳出局部最优的搜索策略。成功地把该算法应用于神经网络训练的基本问题:异或问题、N位奇偶校验和编码解码问题,并与传统的BP算法进行比较。仿真实验证明其性能较传统的GD算法和LM算法更为优越。     

Assessment of the quality of decisions worked out by an artificial neural network (DIAG) which diagnoses a technical object

The issues of the examination and determination of the ambiguity of the identification of the states of a technical object in a diagnostic system with an artificial neural network are presented in this article. It is an important problem in the operation of each inference (decision) system, in which various types of decisions are worked out, particularly including ones for diagnostic systems. For this purpose, a diagnostic system including its elements, in which an artificial neural network is used, is characterized and described. The structure together with the algorithm of a diagnostic neural network is presented. A diagram was drawn up, and the circulation of information in the diagnostic system was described in the perspective of errors brought into decision information by the individual elements of this system. A formula for the general error during working out of decisions in the system is put forward. It was also indicated that a number of factors including interferences and influence of the environment, errors during the measurement of the values of the properties of diagnostic signals, errors at the determination of the ranges of possible (permissible and limiting) changes of the properties of diagnostic signals defined in the inference rules and the accuracy of the drawing up of diagnostic inference rules have all a direct impact on the ambiguity level of the identification of the states of a technical object. In the present article, it is also the possibilities that are put forward for the determination and optimization of the ambiguity of the identification of the states of a technical object. For this purpose, the function that determines the quality of the identification and non-identification of the object’s state was defined in this article. A practical assessment method of the ambiguity level of the identification of the states of a technical object in the examined diagnostic system is additionally presented in an example with the use of a radar system.     

基于改进BP神经网络的电子政务内网信息安全评估方法

为了发现电子政务内网的信息安全隐患,提出一种采用改进反向传播人工神经网络（BP ANN）技术的电子政务内网信息安全的评估方法,基于改进BP ANN建立电子政务内网神经网络评估模型.以电子政务内网主要信息安全指标作为训练样本,对建立的BP ANN评估模型进行学习和训练,找到输入与输出之间的关系,并用样本对训练好的BP网络进行验证.仿真结果表明,评估方法能够较好的为复杂的电子政务内网进行信息安全评估,评估模型稳定且自适应性强.     

An intelligent composite system for statistical process control

Combining statistical process control, artificial neural networks and an expert system for the intelligent analysis and control of a plastic extruder facility is described. Statistical methodology is compared and contrasted to the exploratory neural network technique, which learns to relate and classify dependent production variables based on measurements taken on-line during the process. Integrating the neural network analysis into a composite control system using an expert system is presented.     

线性神经网络及在系统辨识中的初步应用

该文从基本的智能控制技术——神经网络(NN)技术出发,探讨了神经网络用于系统辨识与建模的基本理论,分析了线性系统神经网络建模的规律,提出了一种利用线性神经网络进行系统辨识的方法。该辨识方法显示出很强的处理问题的能力,无需辨别系统阶次,辨识结构简单,收敛速度快,仿真结果表明这种方法的有效性和可行性。该文共分为四部分,第一部分介绍了神经网络用于系统辨识的特征,第二部分讲述了线性神经网络的工作原理,包括线性神经网络的模型、传递函数、学习规则及训练过程,第三部分讲述了线性神经网络进行系统辨识的仿真实例,第四部分对上述内容作了简要小结。     

Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks

This paper describes a genetic system for designing and training feed-forward artificial neural networks to solve any problem presented as a set of training patterns. This system, called GANN, employs two interconnected genetic algorithms that work parallelly to design and train the better neural network that solves the problem. Designing neural architectures is performed by a genetic algorithm that uses a new indirect binary codification of the neural connections based on an algebraic structure defined in the set of all possible architectures that could solve the problem. A crossover operation, known as Hamming crossover, has been designed to obtain better performance when working with this type of codification. Training neural networks is also accomplished by genetic algorithms but, this time, real number codification is employed. To do so, morphological crossover operation has been developed inspired on the mathematical morphology theory. Experimental results are reported from the application of GANN to the breast cancer diagnosis within a complete computer-aided diagnosis system.     

Diagnostic system with an artificial neural network in diagnostics of an analogue technical object

This paper presents a method to control an operation process of a complex technical object, for example a car engine, with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information, which is provided by the sets of information concerning the elements of the basic modules and their output signals. The article also covers a diagnostic system which uses a DIAG computer programme for the recognition of the states of technical objects in trivalent logics. A programme was presented and described in an analytical form for diagnosis through an artificial neural network (ANN), which recognises the states of reparable technical objects in trivalent logics. The final results obtained from the computations conducted by the DIAG programme are presented in the table of the states of the object.     

基于MSP430F448的压力智能变送器的设计与实现

针对硅压阻式传感器输出信号的非线性和温度失调,提出了以仪用嵌入式微处理器MSP430F448为核心,利用神经网络进行非线性补偿的智能压力变送器的设计方案。文章描述了智能压力变送器的系统架构,着重阐述了对压阻式传感器输出信号智能补偿的原理。测试结果表明经补偿后的模拟输出有良好的线性特性,变送器的输出精度达到了0.72%。     

基于组件技术和遗传小波神经网络的智能建模系统研究

本文对已有的人工神经网络、小波分析、遗传算法的建模方法进行组合利用和加以改进,建立了智能信息处理器。该系统将大量的观测数据进行小波去噪等预处理后,作为小波神经网络模型的输入训练样本数据,网络训练中利用遗传算法动态修改网络结构和参数,并避免神经网络训练速度慢、容易陷入局部极值的缺点,从而完成数据挖掘和复杂的非线性建模功能;同时以智能信息处理器为基础,基于GIS平台利用组件技术建立扩展性强的智能建模系统。最后以某灌区水资源管理过程中的径流预报为例进行仿真实验,验证了方案的可行性和有效性。     

人工神经网络在ERP系统中的应用

在传统的ERP的基础上,增加专家系统模块,即基于人工神经网络技术的预测分析模块,提出了ERP和专家系统的集成管理方法,完成复杂的非线性预测,以使ERP系统智能化、自动化水平更高。该模块采用反向传输BP神经网络模型来实现,通过网络的自适应学习和训练,找出输入和输出之间的内在联系,以求解问题。利用该专家系统对汽车制造企业市场销售量进行预测,结果表明：该方法性能、实用性和通用性好。     

A comparative investigation of non-linear activation functions in neural controllers for search-based game AI engineering

The creation of intelligent video game controllers has recently become one of the greatest challenges in game artificial intelligence research, and it is arguably one of the fastest-growing areas in game design and development. The learning process, a very important feature of intelligent methods, is the result of an intelligent game controller to determine and control the game objects behaviors’ or actions autonomously. Our approach is to use a more efficient learning model in the form of artificial neural networks for training the controllers. We propose a Hill-Climbing Neural Network (HillClimbNet) that controls the movement of the Ms. Pac-man agent to travel around the maze, gobble all of the pills and escape from the ghosts in the maze. HillClimbNet combines the hill-climbing strategy with a simple, feed-forward artificial neural network architecture. The aim of this study is to analyze the performance of various activation functions for the purpose of generating neural-based controllers to play a video game. Each non-linear activation function is applied identically for all the nodes in the network, namely log-sigmoid, logarithmic, hyperbolic tangent-sigmoid and Gaussian. In general, the results shows an optimum configuration is achieved by using log-sigmoid, while Gaussian is the worst activation function.     

基于模糊神经网络和D—S推理的智能特征信息融合研究

给出了一种新的分布式多传感器智能特征信息融合系统结构，利用模糊神经网络技术把环境信息和专家语言信息引入融合系统，提出了一种新的智能特征信息融合算法。     

基于 BP 神经网络的铁路互联网售票系统信息安全评估方法

互联网售票逐步取代了传统售票方式,在铁路运输生产中发挥至关重要的作用,但由于其向互联网提供服务,面临多个层面的安全风险和威胁,受外部攻击、病毒感染等安全威胁日益增大,一旦遭受攻击或其他因素导致系统宕机或终止服务,产生社会负面影响巨大。针对上述威胁,需要安全维护人员运用科学的方法和手段,系统地分析系统所面临的威胁及其存在的脆弱性,评估安全事件一旦发生可能造成的危害程度,提出有针对性的抵御威胁的防护对策和整改措施,将风险控制在可接受的水平,最大限度地保障信息系统安全。人工神经网络具有常规方法所不具备的智能特性,具有自主获取和学习知识的功能,可以较好地处理不确定性和非线性的问题,目前基于人工神经网络的信息安全风险评估在多个行业中已经开展了研究并得到了应用。相对其他人工神经网络模型,BP 神经网络模型具有较强的非线性映射能力和自学习、自适应能力。首先,采用3层的神经网络能够以任意精度逼近任何非线性连续函数,使其适合于求解内部机制复杂的问题;其次,训练时能够通过学习自动提取输出、输出数据间的“合理规则”,并自适应的将学习内容记忆于网络的权值中。因此,文章根据铁路互联网售票系统复杂网络体系结构,采用具有3层结构的 BP 反向传播人工神经网络模型与之对应,准确反映互联网售票系统面临的各类安全威胁,并利用 BP 神经网络良好的自适应性和容错能力,以互联网售票系统面临的安全风险威胁等级值为训练样本,采用已训练的 BP 网络对互联网售票系统进行安全风险评估,设计了基于 BP 神经网络的风险评估模型,仿真结果表明,设计的模型具有很好的自适应性和容错能力,适用于复杂的互联网售票系统网络,实验数?     

Pattern recognition and control by adaptive methods for an intelligent mobile vehicle

This paper describes adaptive methods for both pattern recognition and control in an experimental mobile vehicle (MV). An adaptive resonance theory (ART) neural network is used as the character recognizer. It can self-organize and self-stabilize in response to complex binary input vectors. New input patterns can be saved in such a fashion that the stored patterns are not forgotten or destroyed. By merging the advantages of the feed-forward neural network, adaptive algorithm, and fuzzy control, a neuro-fuzzy system also is proposed. This can deal with a large amount of training data in the neural network, from these data produce more reasonable fuzzy rules with the adaptive algorithm, and then control the object by fuzzy control. This is not a simple combination of the three methods, but a merger into one intelligent control system. Finally, the experimental results and some conclusions are given. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002