Parallel Gray Neural Network (PGNN) is an advanced artificial intelligence model that simulates the human brain"s functioning. PGNN achieves efficiency through parallelism and grayscale processing of data, leading to accurate predictions and decisions. This integration of parallel computing and grayscale information processing makes PGNN an innovative approach in the field of neural networks. It has practical applications in various industries, including finance, healthcare, and technology. For example, in finance, PGNN can analyze large volumes of financial data in real-time for risk assessment and investment strategies. In healthcare, it can assist in medical image analysis and diagnosis. In technology, PGNN plays a crucial role in optimization and control systems. The versatility and effectiveness of PGNN make it a powerful tool in driving innovation and solving complex problems across different domains.
并联灰色神经网络,parallel gray neural network
1)parallel gray neural network并联灰色神经网络
1.By usingparallel gray neural network,this paper discusses the effective prediction of foundation pile settlement and data remedy in static tests under the similar geological conditions,and offers the calculation methods for them.本文旨在通过并联灰色神经网络模型,探讨静荷载试验中相似地质条件下基桩沉降量的有效预测和数据修补问题,并给出计算方法。
英文短句/例句
1.Research on the Prediction of Settlement of Foundation Pile with Parallel Gray Neural Network基于并联灰色神经网络的基桩沉降量预测研究
2.Prediction of Ship Movement Based on Parallel Grey Neural Network;基于并联型灰色神经网络的舰船运动预报
3.Prediction of irrigation water use using parallel gray neural network灌溉用水量的并联型灰色神经网络预测
4.Study on Port Throughput Forecasting Method Based on Parallel Grey Neural Network Model;基于并联型灰色神经网络模型的港口吞吐量预测方法探讨
5.Forecast of Hydraulic Discharge Based on Grey Neural Network Inseries Peg Model基于灰色神经网络串联组合模型的涌水量预测
6.Research on Performance and Emission of Electronic Gasoline Engine Based on Grey Relational Analysis and Artificial Neural Network基于灰色关联度和神经网络的电控汽油机性能和排放的研究
7.Dynamic simulation of soil water-salt using BP neural network model and grey correlation analysis土壤水盐动态的BP神经网络模型及灰色关联分析
8.BP artificial neural network model of groundwater dynamic and analysis on the improved grey slope coefficient correlation degree地下水动态的BP神经网络模型及改进的灰色斜率关联度分析
9.Research on marine diesel engine"s state prediction based on series grey artificial neural network基于串联灰色神经网络的船用柴油机状态预测研究
10.Prediction of the productivity of horizontal wells based on gray-relation analysis and neural network基于灰色关联与神经网络技术的水平井产能预测
11.Prediction of Water Surface Evaporation Based on Grey-relation Analysis and RBF Neural Network基于灰色关联分析与RBF神经网络的水面蒸发量预测
12.Neural Network Prediction Model of Rolling Force Based on Grey Incidence Analysis基于灰色关联分析的神经网络轧制力预报模型的研究
13.POPULATION FORECAST BASED ON COMMBINATION OF GRAY FORECAST AND ARTIFICIAL NEURAL NETWORKS;基于灰色预测和神经网络的人口预测
14.Gray-BP artificial neural network forecasting method prediction on water bloom基于灰色-BP神经网络的水华预测方法
15.Prediction of coal mine accidents based on gray elman neural networks基于灰色Elman神经网络的煤矿事故预测
16.Stability of Shunting Inhibitory Cellular Neural Networks with Delays时滞并联限制细胞神经网络的稳定性
17.Research on Analytical Method with Grey System and Neural Network and Their Application;灰色系统与神经网络分析方法及其应用研究
18.Prediction of Stress Concentration Based on Gray Theory and Neural Network;基于灰色理论及神经网络的应力集中预测
相关短句/例句
parallel gray neural networks并联型灰色神经网络
3)Fuzzy-gray relating-neural network模糊-灰色关联-神经网络
4)the series grey neural network prediction model串联灰色神经网络模型
5)grey neural network灰色神经网络
work traffic prediction based ongrey neural network integrated model;一种基于灰色神经网络的网络流量预测模型
2.The Establishment of the Grey Neural Network Model and Its Application on the Problem of Complex Nonlinear Prediction;灰色神经网络模型的建立及其在复杂非线性预测问题中的应用
3.Prediction on the deformation of soft rock roadway usinggrey neural network method软岩巷道变形灰色神经网络多步预测研究
6)Gray neutral network灰色神经网络(GNN)
延伸阅读
基于模糊神经网络的模具产品报价系统一、报价系统概论产品报价是指被讯价方根据自身所处市场环境、生产、经营、管理现状等因素而针对讯价方所指定的产品及其特殊的功能需求所报出的价格。产品报价是一种复杂而有重要的经济行为。产品报价的高低好坏有利于报价双方能面对面坐下来并经多次商讨而确定产品的成交价格并最终达成协议，签订合同。产品报价[1]，特别是比较复杂的产品报价，如模具产品报价，需要许多领域人员的协调工作，如技术、财务、商务等，必须考虑各种结构化和非结构化的因素。其中结构化因素如技术参数、结构参数、工艺参数、制造成本、费用分配比例等比较易于确定的因素。而非结构化因素如最终利润率、赢得订单的几率等，则需要考虑企业内外环境等各种不确定因素。从信息系统角度来考虑，整个报价过程是一个信息流动和信息处理的过程，包括信息的产生、传递、处理、存储；具有很复杂的信息流，涉及到销售、经营、设计、会计、生产计划、采购等等。[1]目前国内外开发的报价系统依其功能可大致分为五类，即商务型报价系统、生产型报价系统、工程型报价系统、投标型报价系统和集成型报价系统。工程型报价系统实际上是产品选型、初步设计加成本估算，其最终报价的形成有待提高；商务型报价系统，是在技术报价的基础上，对产品价格进行分析、计算、结合价格变化趋势预测的结果，确定合适的产品价格。其全部价值是基于产品成本而做的加价判断或推理。二者各自突现了自己的重点，如前者对报价的结构化问题处理较好，而后者对报价所涉及的非结构化因素研究较为深刻。二、模具产品的报价模具产品的报价是一个非常复杂的过程。但从单纯的仅考虑结构化因素的技术报价来看。框一、功能分解与评价：根据客户提供的工件图纸及交货期限、或其他特殊的要求分析工件的结构特征、工艺参数等因素，提取有用信息。框二、产品方案设计：根据功能评价所提供的有用信息及交货期限等，考虑自身的生产、经营、管理现状，确定合理的方案。主要有工件排样、模具类型选择、压力机参数估算选型等。框三、结构设计：根据设计方案确定模具的合理结构和大致尺寸，同时选定模架形式等。框四、成本估算：根据工厂积累的有关经验数据（如外构件的价格、人工费用、材料费用、费用分配比例等）和以往开发同类产品的报价经验，由结构设计和方案设计所得的有关信息，估算产品成本。框五、历史经验资料、数据：为方案、结构、成本估算提供各种所需的资料、数据。包括各种工具书、国家标准、材料费用表、人工费用表、费用分配比例、以往开发经验及相关数据等非常有用的各种信息。
