Intelligent Control Mechanism of Part Picking Operations of Automated Warehouse

This paper studies the part picking operations of a ut omated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are kn own, it is necessary to decide an efficient order picking sequence and routing t o minimize the total travel distance to complete those orders. Assumed there are n i items to be picked in order O i. Each item in the picking ord er is located in different locations in the warehouse. Since i...     

发酵过程混合神经网络模型及其仿真

提出了一种新型的发酵过程混合神经网络模型，该模型由非线性神经网络和线性神经网络两部分组成，由于非线性神经网络采用结构具有线形式的Flat网络，两个网络能够合并为同一表达式，并具有线性形式，可采用线性最小二乘法求解网络权值，与串联结构及串并联结构混合神经网络模型相比，该模型训练方式简单，并可方便地使用在线辨识算法。     

基于RBF网络的混沌时间序列的建模与多步预测

提出将RBF神经网络应用于混沌时间序列的建模与预测中 ,设计了一个三层RBF网络结构 ,说明了RBF网络用于混沌时间序列建模和预测时的基本性质。仿真结果表明 ,RBF网络模型对混沌时间序列有比较强的拟合能力和比较高的一步及多步预测精度。采用RBF网络进行混沌时间序列的建模和预测能够取得比其它方法好得多的效果。     

基于变神经网络的非线性最小方差预测控制器

提出基于变神经网络学习动态系统参数的最小方差预测控制器。其目的是通过在线学习 ,使控制器(MVPC)能适应被控对象参数变化和非确定性。提出的变神经网络由两部分组成 ,一部分是线性神经网络 (LNN) ,作为被控对象局部线性动态模型 ,另一部分是多层交叉回归神经网络 (DRNN) ,它近似为非线性动态模型。由于引进递推最小方差算法 ,本控制器运算速度相当快。仿真结果表明所提方法对非线性系统自适应控制是有效的     

人工神经元网络及其在系统仿真中的应用

本文简要地介绍了人工神经元网络的基本概念、主要特征及其发展历史,评述了几种有代表性的神经元网络模型。在此基础上,探讨了人工神经元网络在系统仿真中可能的应用方向。     

神经网络自适应滤波器在故障诊断中的应用

分析了用自适应滤波器进行故障诊断的方法．针对该方法在计算上要花费大量时间、难于完成实时诊断任务的缺点，提出了用神经网络自适应滤波器来完成故障诊断的方法．神经网络有极快的运行速度，能很好地完成实时诊断任务．     

希尔伯特变换器优化设计研究

提出了一种基于神经网络算法的希尔伯特变换器优化设计方法，证明了神经网络算法的收敛性，提出了希尔伯特变换器的优化设计实例。仿真结果表明了该网络模型是高效的神经网络模型。     

Research on Digital Models for Product Design and Manufacture

CAD/CAM integrate technology has been drawn to much a ttention and becomes one of the focus of the international academic and commerci al research, Through efforts nearly 10 years, remarkable progress has been made in the 3-dimension feature parametric modeling. UG, SOLIDWORK and MDT apply the se technology with respective features. However, no distinct development has bee n made in the digital models from geometric feature to manufacture feature. Prod uct information expression and the basic model frame ...     

Accurate forecasting of the undecided population in a public opinion poll

The problem of pollsters is addressed which is to forecast accurately the final answers of the undecided respondents to the primary question in a public opinion poll. The task is viewed as a pattern‐recognition problem of correlating the answers of the respondents to the peripheral questions in the survey with their primary answers. The underlying pattern is determined with a supervised artificial neural network that is trained using the peripheral answers of the decided respondents whose primary answers are also known. With peripheral answers as inputs, the trained network outputs the most probable primary response of an undecided respondent. For a poll conducted to determine the approval rating of the (former) Philippine president, J. E. Estrada in December 1999 and March 2000, the trained network predicted with a 95% success rate the direct responses of a test population that consists of 24.57% of the decided population who were excluded in the network training set. For the undecided population (22.67% of December respondents; 23.67% of March respondents), the network predicted a final response distribution that is consistent with the approval/disapproval ratio of the decided population. Copyright © 2002 John Wiley & Sons, Ltd.