弧焊离线编程系统中计算机辅助编程技术

从为实现离线编程系统中计算机自动编程目标出发,对焊接工件特征建模、焊接参数规划技术进行了研究.研究了综合特征识别、特征定义与特征设计的焊接工件特征建模技术,并在SolidWorks之上开发了焊接工件特征建模器.采用基于事例推理和人工神经网络等人工智能技术,开发了用于机器人弧焊的焊接参数过程规划器.     

基于Hopfield神经网络的腐蚀失效模式识别

Hopfield神经网络是具有记忆功能的反馈型神经网络，但存储记忆的能力受到神经元数量的限制。本文根据Hopfield神经网络的特点，采用多模块分级识别的方法，研究开发适用于腐蚀失效模式识别的学习、推理机模型。在此基础上，运用面向对象的编程技术及数据库技术，实现能够学习、识别腐蚀失效模式的软件系统。     

面向电子压力机模糊神经网络PID控制的研究

针对电子压力机位置伺服系统的非线性和时变的不确定性,压装力、压装速度和压入深度高可控性,系统的高稳定性、适应性及较强的抗干扰能力等特点,提出将神经网络实现模糊PID自调整的控制特性应用在现存的小型电子压力机的位置伺服系统中的方法。该控制策略将模糊控制的推理能力和神经网络的学习能力进行了有效的结合,其中,PID控制器参数自调整是通过学习并记忆PID参数调整的基本规则来实现的,以满足电子压力机位置伺服系统的要求并用MATLAB软件编程进行仿真分析。仿真结果表明:相比较常规神经网络与传统PID相结合组成的控制器,模糊神经网络PID自调整控制器对于电子压力机的位置伺服系统具有更快的响应特性及更好的稳定性。     

Application of neural networks to an expert system for cold forging

The technique of neural networks is applied to an expert system for cold forging in order to increase the consultation speed and to provide more reliable results. A three-layer neural network is used and the back-propagation algorithm is employed to train the network.

By utilizing the ability of pattern recognition of neural networks, a system is constructed to relate the shapes of rotationally symmetric products to their forming methods. The cross-sectional shapes of the products which can be formed by one blow are transformed into 16 × 16 black and white points and are given to the input layer. After learning about 23 products, the system is able to determine the forming methods for the products which are exactly the same or slightly different from the products used in the network training. To exploit the self-learning ability, the neural networks are applied to the prediction of the most probable number of forming steps, from information about the complexity of the product shape and the materials of the die and billet, and also to the generation of rules from the knowledge acquired from an FEM simulation. It is found that the prediction of the most probable number of forming steps can be made successfully and that the FEM results are represented better by the neural networks than by the statistical methods.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

涡流检测信号处理技术

涡流检测线圈输出信号十分复杂,对该信号进行准确的分析处理是获得高精度和高可靠性检测结果的基础。介绍了涡流检测信号分析处理的几种新技术,包括小波除噪技术、神经网络技术、信息融合技术、电磁场仿真技术、DSP技术和网络分析处理系统等。通过分析各种新技术的应用状况及发展潜力,指出了将这些技术综合运用并与专家系统相结合实现智能检测是未来的发展趋势。     

Developing of an expert system for nonferrous alloy design

Expert systems have been used widely in the predictions and design of alloy systems. But the expert systems are based on the macroscopic models that have no physical meanings. Microscopic molecular dynamics is also a standard computational technique used in materials science. An approach is presented to the design system of nonferrous alloy that integrates the molecular dynamical simulation together with an expert system. The knowledge base in the expert system is able to predict nonferrous alloy properties by using machine learning technology. The architecture of the system is presented.     

模糊神经专家系统在风机实时故障诊断中的应用

实时性和准确性在风机实时状态监测与故障诊断中起着决定性的作用.本文综合运用模糊处理、神经网络和专家系统等先进的诊断方法及信号处理技术,采用故障定性--原因确定--处理方案等过程,提出了一种新型的模糊神经专家状态监测与故障诊断方法,并采用先进的Visual C 语言及模块式设计,为提高故障诊断准确性和实时性提供可靠保证.     

Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases

This paper describes RAPTURE—a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert rule bases demonstrate that this combined approach generally performs better than previous methods.     

焊接工艺参数的神经网络智能设计

将人工神经网络技术引入焊接专家系统,建立 多种焊接规范参数设计网络模型。以实际焊接工艺数据对所建网络模型进行训练,经测试表明,网络具有很强的自学习自适应能力、容错与协调能力以及显著的联想功能。本文有效解决了焊接工艺设计专家系统的关键困难－－焊接规范参数设计,为焊接专家系统研究开辟了新的方向。     

An Approach to Combining Explanation-based and Neural Learning Algorithms

Machine learning is an area where both symbolic and neural approaches to artificial intelligence have been heavily investigated. However, there has been little research into the synergies achievable by combining these two learning paradigms. A hybrid system that combines the symbolically-oriented explanation-based learning paradigm with the neural backpropagation algorithm is described. In the presented EBL-ANN algorithm, the initial neural network configuration is determined by the generalized explanation of the solution to a specific classification task. This approach overcomes problems that arise when using imperfect theories to build explanations and addresses the problem of choosing a good initial neural network configuration. Empirical results show that the hybrid system more accurately learns a concept than the explanation-based system by itself and learns faster and generalizes better than the neural learning system by itself.     

点焊工艺设计智能混合系统研究

将计算机和人工智能技术引入点焊工艺设计,综合运用人工神经网络（ＡＮＮ）、基于事例失推理（ＣＢＲ）、模糊系统（Ｆｕｚｚｙ）、基于事例的学习（ＣＢＬ）以及产生式专家系统（ＥＳ）等多种智能方法,建立了一个点焊工艺设施有混和系统（ＩＳＳＷ）。工艺实验表明,本文所研制的ＩＳＳＷ可以满足点焊工艺基本要求。     

模糊神经网络在感应电机矢量控制中的应用

本文提出了一种基于模糊神经网络速度控制器（FNNC）的感应电机矢量控制系统,兼具模糊逻辑处理不确定信息的能力和神经网络的自学习能力,阐明了神经网络的结构设计、样本选取及训练方法。人工神经网络（ANN）的初始权值和阈值通过离线学习得到,模糊逻辑规则通过专家经验总结。仿真结果表明采用所提出的模糊神经网络的感应电机矢量控制系统,转速响应快,跟踪性能好,稳态误差大大减小,有效提高了系统的性能。     

基于遗传算法和神经网络的软脆工件研磨加工工艺智能决策系统

目的解决研磨抛光工艺决策中工艺试验耗时耗力的问题,实现在研磨抛光加工中根据加工工艺参数对加工质量进行预估。方法采用遗传算法优化的BP神经网络为主要算法,构建智能预测模型,建立研磨加工中输入参数和输出参数之间的映射关系。然后收集有效的输入参数和输出参数作为网络训练和测试的样本数据集,通过遗传算法对神经网络的初始化权值和偏置进行优化,用样本数据集训练神经网络。同时,在决策系统的理论基础上,将神经网络与决策系统进行结合,利用神经网络的学习能力建立智能决策的数据库和规则库,最终建立智能决策系统。结果与无改进的BP神经网络的决策方法相比,无论是在预测精度,还是学习速度上,遗传算法优化的神经网络性能更加优异,决策系统的决策效果更好。结论研磨加工工艺智能决策系统是可行的,为研磨加工的工艺决策提供了一种新的思路。     

基于专家系统和神经网络的制造过程智能决策系统

提出一种基于专家系统和神经网络相结合的加工过程多目标优化智能决策方法,建立了专家系统和神经网络之间的信息交换机制,采用面向对象的方法设计了车削加工过程多目标优化的智能决策系统。     

Adaptation of Learning Rule Parameters Using a Meta Neural Network

This paper proposes an application-independent method of automating learning rule parameter selection using a form of supervisor neural network (NN), known as a meta neural network (MNN), to alter the value of a learning rule parameter during training. The MNN is trained using data generated by observing the training of a NN and recording the effects of the selection of various parameter values. The MNN is then combined with a normal learning rule to augment its performance. Experiments are undertaken to see how this method performs by using it to adapt a global parameter of the resilient backpropagation and quickpropagation learning rules.     

Kinematic motor learning

This paper focuses on adaptive motor control in the kinematic domain. Several motor-learning strategies from the literature are adopted to kinematic problems: ‘feedback-error learning’, ‘distal supervised learning’, and ‘direct inverse modelling’ (DIM). One of these learning strategies, DIM, is significantly enhanced by combining it with abstract recurrent neural networks. Moreover, a newly developed learning strategy (‘learning by averaging’) is presented in detail. The performance of these learning strategies is compared with different learning tasks on two simulated robot setups (a robot-camera-head and a planar arm). The results indicate a general superiority of DIM if combined with abstract recurrent neural networks. Learning by averaging shows consistent success if the motor task is constrained by special requirements.     

基于观测器和神经网络的液压系统故障诊断方法研究

针对液压系统特点,提出基于观测器和神经网络的故障诊断方法.该方法的原理是基于观测器实现故障的判断,利用经观测器输出训练的神经网络实现故障定位.相对于故障树和专家系统等方法,该方法的优点是诊断速度更快、不需要大样本.仿真结果证明该方法有效.     

基于人工神经网络的气门电热镦粗工艺专家系统

由于气门电热镦粗过程复杂,其成形过程的工艺参数大多是借助于经验选择。文章针对不同的工艺参数,不同的确定方式,提出了一种采用传统人工智能方法和人工神经网络相结合的混合专家系统,将逻辑思维推理的知识和形象思维推理的知识结合起来,避免了各自的不足,得到了较好的工艺参数确定方式。该专家系统采用原型的知识表示方式,运用面向对象的程序设计方法开发整个软件,为气门电镦提供了便于维护的确定较合理工艺控制参数的系统。     

用神经网络算法预测氢蚀孕育期

利用神经网络的分析方法，对高温氢腐蚀进行分析建模，综合考虑温度、氢分压和氢腐蚀孕育期之间的关系，为准确的数学建模、预测设备的使用寿命和相应专家系统的研制开拓了新的思路。