基于神经网络的汉语文法分析专家系统的设计

本文提出了一种基于神经网络的汉语文法分析专家系统的设计方案。知识库采用产生式规则的知识表达方式，并将知识二元化存储在神经网络中。推理机采用神经网络进行推理。在论文的结尾给出了系统的运行实例，说明了该系统的有效性。     

基于单调指标空间方法的分布式信息处理系统指标分析与设计

介绍了一种新的系统能力指标分析与设计的方法:单调指标空间方法,并运用其于一抽像分布式信息系统的指标分析与设计.计算结果表明,这种方法具有较好的适用性和良好的应用前景.     

基于JMP的神经网络设计实例分析

本文利用JMP对一个复杂多项式进行拟合,从实验设计产生样本数据、神经网络设计、训练样本技巧、到结果分析进行详细阐述。使读者能够使用JMP软件的神经网络分析工具,解决实际问题。     

基于神经网络的渔业养殖成本与效益分析和预测

在单一的养殖模式下,由于破坏了生物多样性,这使得生态系统处于非常不稳定的状态,并对其他物种的生存,如珊瑚礁也产生了巨大影响。虽然混合水产养殖是一个有效的解决办法,但由于混养系统中的食物链的复杂特征,并没有有效的关于如何预测成本与效益的预测方法。本文试图从神经网络角度入手,为用户提供完整,准确的预报服务。     

基于神经网络与组成份分析的关联规则挖掘

通过对关联规则挖掘的分类研究,提出了关联规则挖掘的分类定义方法,从项集的相关性出发,阐述了一种基于ANN-PCA的快速、高效的挖掘算法,从而扩展了关联规则挖掘的应用。实验证明了该算法的有效性。     

基于神经网络与遗传算法的传动部件设计优化

为了提高设计效率,得到最优设计效果。依据人工神经网络模型和遗传算法的基本原理,建立了基于神经网络和遗传算法的传动部件设计优化模型,用Matlab语言编制了应用程序,对神经网络的参数预测与遗传算法的优化过程进行求解,试验仿真结果表明,采用设计优化切实可行,具有良好的实际应用效果。     

基于BP神经网络的隐写分析分类器设计

设计并实现了基于BP神经网络的隐写分析分类器。首先对图像库中的图像进行格式变换,并使用扩展修改方向和钻石编码两种隐写方法进行不同嵌入率的隐写嵌入,然后计算载体图像和载密图像中平面域、DCT域和小波域的一些属性值作为特征。利用Matlab的模式识别工具箱搭建BP神经网络,用已知类别的图像特征训练分类器并进行分类测试。实验结果表明,多域综合特征可以实现良好的分类效果,能以较高的准确率识别出载体图像和载密图像。     

基于正交基的小波神经网络的优化设计

文章提出一种基于多分辨率学习的正交基小波神经网络结构的设计方法，网络权值的学习采用阻尼递推最小二乘算法。该方法能有效地解决训练精度和泛化能力差的问题。仿真结果表明它是一种非常有效的小波神经网络结构的设计方法。     

基于神经网络的虚拟气体分析仪设计

针对在进行红外二氧化碳气体分析时易受温度漂移等影响造成结果不准确的特点,文中提出利用神经网络技术进行红外二氧化碳气体分析,以提高分析的准确性,同时利用虚拟仪器开发平台Labwindows/CVI进行虚拟红外二氧化碳气体分析仪的开发设计.     

基于神经网络的多模态控制器设计

基于神经网络所具有的定性推理和定量数值并行计算能力,以及学习记忆能力,集成非线性 多个特征模型和控制器,实现了控制系统的多模智能控制,该方法充分结构的定性知识和定量数学描述信息,实现了参数大范围变化时变系统的良好控制,最后用该方法对某型激光制导炸弹设计了一多模态控制器,仿真结果表明了该方法的优良性能。     

基于神经网络集成的软件故障预测及实验分析

软件系统故障预测是软件测试过程中软件可靠性研究的重点之一。利用软件系统测试过程中前期的故障相关信息进行建模,预测后期的软件故障信息,以便于后期测试和验证资源的合理分配。根据软件测试过程中已知的软件故障时间序列,利用非齐次泊松分布过程、神经网络、神经网络集成等方法对其进行建模。通过对三个实例分别建模,其预测平均相对误差G-O模型依次为3.02%、5.88%和6.58%,而神经网络集成模型为0.19%、1.88%和1.455%,实验结果表明神经网络集成模型具有更精确的预测能力。     

A novel function-structure concept network construction and analysis method for a smart product design system

The rapid growth of data and the requirement of designers to track massive data to obtain design stimuli have posed challenges to conceptual design, thereby promoting the development of data-driven design. Concept networks precisely capture design information from a large volume of unstructured and heterogeneous textual data and saliently decrease time and labor cost for designers to read texts, which creates new opportunities for developing a smart product design system. To advance data-driven design, this study proposes the novel function-structure concept network (FSCN) construction method, which combines sentence parsing and word/phrase extraction to integrate functional and structural information. Furthermore, a network analysis method is proposed to explore design information associations that contain both explicit and implicit associations together and thereby recommend them simultaneously to designers as inspirational stimuli to support design ideation. This approach can enhance designers' capabilities to build associations between design information, conceive new design ideas during conceptual design, and increase creativity for solving design problems. The proposed FSCN construction and analysis method can be used as an auxiliary tool to visualize associations among design information so as to inspire idea generation in the early stage of conceptual design. An illustrative example was used to validate the practicability of the proposed methodology. The code of the proposed method is available at https://github.com/KWflyer/FSCN.     

神经网络结构动态优化设计的分析与展望

阐述了神经网络结构设计对神经网络性能的影响. 介绍了动态结构神经网络, 尤其是增长型和修剪型神经网络研究的发展过程, 分析了动态设计方法研究在计算能力、学习理论和网络的稳定性等方面取得的成果. 最后对神经网络动态设计的研究进行总结, 给出了神经网络结构动态设计研究的发展趋势.     

Push button design requirements and relations to button architecture elements

The automotive industry supply chain has been subjected to new and global competition pressures, which are pushing for collaborative, interdependent and strategic product development partnerships among its agents, with the objective of improving the processes and responsiveness to product engineering requirements. Thus, some research has been dedicated to the analysis and translation of these user needs into product engineering requirements in order to provide methods to increase the conversion responsiveness. However, there is an additional conversion phase which is the definition of the product architecture to meet those requirements. So, the objective of this paper is to present a method and case study where button parameters are related to its architecture elements by applying Morphological Analysis and Artificial Neural Networks on the data collected from measurements made on in – car radio interfaces. The precision power of the models was demonstrated, meaning that it is possible to have an expected haptic behavior for the button, predicted in the form of a set of relevant haptic characteristics, based on the button architecture, and so, facilitating the initial detail design stages of in-car radio interfaces and potentially increasing its responsiveness to the client needs.     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor

A novel approach is presented for the analysis and the design of a controller for a bioreactor. It is based on the model reference control theory, assisted by a neural network identifier. The control objectives specified in the paper require the controller to be a nonlinear one, however, it is shown that it is stable in the sense of bounded input bounded output and locally stabilizing in the sense of Lyapunov. The feasibility and the efficacy of the proposed approach are tested on the benchmark problem. Copyright © 1999 John Wiley & Sons, Ltd.     

Analysis and design of a real-time manufacturing subsystem

The purpose of this paper is to present a system methodological approach to designing a computer based manufacturing subsystem. The subsystem, a manual job costing system, is explained and a summary of design procedures for converting this manual system to a computer based information system is provided.     

Kinematic analysis and optimal design of a novel 1T3R parallel manipulator with an articulated travelling plate

Driven by the requirements of the large-scale component assemblage for the docking platform, this paper proposes a novel one-translational-three-rotational (1T3R) parallel manipulator with an articulated travelling plate, which can provide high stiffness and good accuracy performances in the assemblage. The underlying architecture of this manipulator is briefly addressed with emphasis on the practical realization of the articulated travelling plate. On the basis of the kinematic analysis of the 1T3R parallel manipulator, its optimal design considering the force and motion transmissibility is carried out, in which the generalized virtual power transmissibility of this manipulator is defined. This paper aims at laying a solid theoretical and technical foundation for the prototype design and manufacture of the 1T3R parallel manipulator.