Adv. Artif. Neural Syst.最新文献_第7页

Fuzzified Data Based Neural Network Modeling for Health Assessment of Multistorey Shear Buildings 基于模糊数据的多层抗剪建筑健康评估神经网络建模

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/962734

Deepti Moyi Sahoo, S. Chakraverty

引用次数: 4

The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods 基于机器学习方法的三维眼球震眼动信号有效和无效节拍的分类

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/972412

M. Juhola, H. Aalto, H. Joutsijoki, T. Hirvonen

引用次数: 5

Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor 序批式反应器生物去除屠宰场废水中有机碳和氮的人工神经网络建模

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/268064

Pradyut Kundu, A. Debsarkar, S. Mukherjee

{"title":"Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor","authors":"Pradyut Kundu, A. Debsarkar, S. Mukherjee","doi":"10.1155/2013/268064","DOIUrl":"https://doi.org/10.1155/2013/268064","url":null,"abstract":"The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models \"A,\" \"B,\" and \"C\") using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models \"A,\" \"B,\" and \"C\") were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"109 3 1","pages":"268064:1-268064:15"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89733369","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: 23

Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations 求解常微分方程的人工神经网络结构比较

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/181895

Susmita Mall, S. Chakraverty

{"title":"Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations","authors":"Susmita Mall, S. Chakraverty","doi":"10.1155/2013/181895","DOIUrl":"https://doi.org/10.1155/2013/181895","url":null,"abstract":"This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of randomas well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"17 1","pages":"181895:1-181895:12"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037343","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: 39

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation Sierpinski衬垫分形天线的人工神经网络分析:一种低成本的实验替代方法

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/560969

B. S. Dhaliwal, S. S. Pattnaik

引用次数: 15

An Efficient Constrained Learning Algorithm for Stable 2D IIR Filter Factorization 稳定二维IIR滤波器分解的高效约束学习算法

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/292567

N. Ampazis, S. Perantonis

引用次数: 1

Estimation of Static Pull-In Instability Voltage of Geometrically Nonlinear Euler-Bernoulli Microbeam Based on Modified Couple Stress Theory by Artificial Neural Network Model 基于修正耦合应力理论的几何非线性欧拉-伯努利微梁静态拉入不稳定电压人工神经网络模型估计

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/741896

M. Heidari, Y. Beni, H. Homaei

{"title":"Estimation of Static Pull-In Instability Voltage of Geometrically Nonlinear Euler-Bernoulli Microbeam Based on Modified Couple Stress Theory by Artificial Neural Network Model","authors":"M. Heidari, Y. Beni, H. Homaei","doi":"10.1155/2013/741896","DOIUrl":"https://doi.org/10.1155/2013/741896","url":null,"abstract":"In this study, the static pull-in instability of beam-type micro-electromechanical system (MEMS) is theoretically investigated. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two supervised neural networks, namely, back propagation (BP) and radial basis function (RBF), have been used formodeling the static pull-in instability of microcantilever beam. These networks have four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data employed for training the networks and capabilities of the models in predicting the pull-in instability behavior has been verified. Based on verification errors, it is shown that the radial basis function of neural network is superior in this particular case and has the average errors of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results ofmodeling with numerical considerations show a good agreement, which also proves the feasibility and effectiveness of the adopted approach.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"703 1","pages":"741896:1-741896:10"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75765976","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: 6

Visualizing Clusters in Artificial Neural Networks Using Morse Theory 基于莫尔斯理论的人工神经网络聚类可视化

Adv. Artif. Neural Syst. Pub Date : 2013-01-01 DOI: 10.1155/2013/486363

Paul T. Pearson

引用次数: 3

Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine 应用于生物科学和医学的无监督学习技术进展

Adv. Artif. Neural Syst. Pub Date : 2012-01-01 DOI: 10.1155/2012/219860

A. Meyer-Bäse, S. Lespinats, J. Górriz, O. Bastien

引用次数: 0

Sleep Stage Classification Using Unsupervised Feature Learning 使用无监督特征学习的睡眠阶段分类

Adv. Artif. Neural Syst. Pub Date : 2012-01-01 DOI: 10.1155/2012/107046

Martin Längkvist, L. Karlsson, A. Loutfi

引用次数: 254