基于FPGA技术的可重构软测量系统

介绍了一种基于FPGA（Field Programmable Gates Array）技术的可重构的软测量系统。该系统由一个单片机控制着一片FPGA芯片，芯片上配置的软测量模型在一个BP人工神经网络模型及其训练模型之间切换（即重构）。该系统在实际中有广泛的用途，如测量工业生产中的过程变量、生物体内部状态、海洋探测、空间设备等。     

人工神经网络预测煤的发热量

应用人工神经网络处理部分广东地区煤质分析数据,建立网络模型,预测出煤的发热量,经实测值与预测值比较,在一定的煤种范围内预测值与实测值比较接近。     

人工智能的原理及应用

介绍了人工智能的发展，并对改进的神经网络专家系统的优越性做了介绍，指出了神经网络专家系统广阔的应用前景及实现。     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

人工神经网络理论及其应用

简要介绍了人工神经网络的发展过程和基本理论，从神经网络具有自学习功能、联想存储功能和高速寻找优化解的能力三个方面论述了其特点和优越性，详细阐述了在模式识别、信号处理、自动控制、人工智能、自适应的人机接口、优化计算、通信以及其它方面的应用，探讨和分析了其发展前景。     

Boole函数的线性可分性(Ⅰ)——n-维超立方体的基本理论

Boole函数的线性可分性是前向人工神经网络理论中的一个比较困难的问题之一。目前仅对变量数n≤7的某些问题给予讨论。当n≥8时,尚无判别Boole函数线性可分的一般准则,更无线性可分Boole函数的计数公式。基于此,本文详细地研究了与Boole函数线性可分性有关的n-维超立方体的基本理论,包括n-维超立方体的基本性质、超立方体中的平行线、子超立方体的计数等,并给出了构造n-维超立方体图的一种新方法。     

高氧化态（Ⅱ─Ⅳ）三核金属簇合物构型的人工神经网络判别方法

本文运用一典型的人工神经网络模型─“反向传播”模型，对高氧化态（Ⅱ─Ⅳ）三核金属簇合物的构型分布进行了分析，得到了较好的分类、预报结果为化合物结构分析提供了新的工具。     

人工神经网络预测热油环道启动压力

高含蜡原油管道停输后的再启动问题非常复杂。它涉及因素多，问题的非线性很强。而有足够多神经元的S型三层神经网络理论上可逼近任意有理函数。文章采用误差反向传播网络预测环道实验装置中的启动压力，得到了较好结果。     

陶瓷窑炉自控智能化有待解决的问题

人工神经网络及模糊控制在陶瓷窑炉自控系统中得到日益广泛的应用,它们自身的发展也不断推动陶瓷窑炉智能控制的开发及应用,但是在陶瓷窑炉自控智能化的进程中仍存在一些问题有待于解决。     

Effects of modeling means on properties of monitoring models o spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality.According to the means of modeling,the known models can be divided into three large categories:single linear regression models,multiple linear regression models and multiple nonlinear models.By modeling the relations between dynamic resistance information and welding quality parameters with different means,this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results,the following conclustons can be drawn:By comparison with two other kinds of models,artificial neural network(ANN) model can describe non-linear and high coupling relationship between monitoring information and quality information more reasonably,improve performance of monitoring model remarkably,and make the estimated values of welding quality parameters more accurate and reliable.