Adv. Artif. Neural Syst.最新文献_第6页

A Unified Framework for GPS Code and Carrier-Phase Multipath Mitigation Using Support Vector Regression 基于支持向量回归的GPS码与载波相位多径缓解统一框架

Adv. Artif. Neural Syst. Pub Date : 2013-03-05 DOI: 10.1155/2013/240564

Quoc-Huy Phan, Su-Lim Tan, I. Mcloughlin, Duc-Lung Vu

引用次数: 17

Inverse Analysis of Crack in Fixed-Fixed Structure by Neural Network with the Aid of Modal Analysis 基于模态分析的神经网络固固结构裂纹逆分析

Adv. Artif. Neural Syst. Pub Date : 2013-03-03 DOI: 10.1155/2013/150209

D. Thatoi, P. K. Jena

引用次数: 4

Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems 求解多响应优化问题的委员会机可取性改进

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/628313

S. J. Golestaneh, N. Ismail, M. Ariffin, S. H. Tang, H. M. Naeini

{"title":"Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems","authors":"S. J. Golestaneh, N. Ismail, M. Ariffin, S. H. Tang, H. M. Naeini","doi":"10.1155/2013/628313","DOIUrl":"https://doi.org/10.1155/2013/628313","url":null,"abstract":"Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percentmore. Consequently, the economic run number of the algorithm is five.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"1 1","pages":"628313:1-628313:9"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88805051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model 多层感知器剪枝参数的方差敏感性分析:在锯木厂供应链仿真模型中的应用

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/284570

P. Thomas, M. Suhner, André Thomas

{"title":"Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model","authors":"P. Thomas, M. Suhner, André Thomas","doi":"10.1155/2013/284570","DOIUrl":"https://doi.org/10.1155/2013/284570","url":null,"abstract":"Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning approaches. The main contribution of this paper is twofold; it first proposes a new pruning algorithm based on an analysis of the variance of the sensitivity of all parameters of the network and then uses this algorithm to reduce the simulation model of a sawmill supply chain. In the first step, the proposed pruning algorithm is tested with two simulation examples and compared with three classical pruning algorithms fromthe literature. In the second step, these four algorithms are used to determine the optimal structure of the network used for the complexity-reduction design procedure of the simulation model of a sawmill supply chain.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"117 1","pages":"284570:1-284570:17"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76001446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model 基于云分辨模式模拟数据的神经网络集成学习气候和数值天气预报模式的随机对流参数化

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/485913

V. Krasnopolsky, M. Fox-Rabinovitz, A. Belochitski

{"title":"Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model","authors":"V. Krasnopolsky, M. Fox-Rabinovitz, A. Belochitski","doi":"10.1155/2013/485913","DOIUrl":"https://doi.org/10.1155/2013/485913","url":null,"abstract":"Anovel approach based on the neural network (NN) ensemble technique is formulated and used for development of aNNstochastic convection parameterization for climate and numerical weather prediction (NWP)models. This fast parameterization is built based on learning fromdata simulated by a cloud-resolvingmodel (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR) Community AtmosphericModel (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"120 1","pages":"485913:1-485913:13"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88296294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 110

Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification 基于离散紧致度的矢量量化网络训练:增强自动脑组织分类

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/278241

R. Pérez-Aguila

引用次数: 1

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network 滑杆气动控制阀的递归神经网络控制

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/410870

M. Heidari, H. Homaei

引用次数: 5

Globally Exponential Stability of Impulsive Neural Networks with Given Convergence Rate 给定收敛速率下脉冲神经网络的全局指数稳定性

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/908602

Chengyan Liu, Xiaodi Li, Xilin Fu