检索结果-内蒙古大学图书馆

Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

引用

MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第4期4卷 259-274页

作者： Onyelowe, Kennedy C. Iqbal, Mudassir Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Michael Okpara Univ Agr Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shangai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China Univ Nigeria Fac Engn Dept Civil Engn Nsukka Nigeria Fed Univ Technol Owerri Dept Environm Technol Owerri Nigeria

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg-Muarquardt backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil's strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (w(L)), (plastic limit (w(P)), plasticity index (I-P), optimum moisture content (w(OMC)), clay activity (A(C)), and (maximum dry density (delta(max)). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models' accuracy.

关键词： Soft computing Artificial intelligence Artificial neural network (ANN) Machine learning in geotechnics back-propagation algorithm Levenberg– muarquardt algorithm Bayesian algorithm Conjugate gradient algorithm Sustainable construction materials

来源：评论

学校读者我要写书评

暂无评论

Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第10期46卷 9451-9464页

作者： Kapoor, Divneet Singh Kohli, Amit Kumar Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala 147004 Punjab India

This paper presents an adaptive-slope squashing-function (ASF)-based artificial neural network (ANN) for efficient estimation of smoothly time-varying multipath fading channels, in a 4x1 space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system using 64 subcarriers. The channel-state-information (CSI) estimated at first stage is further used for OFDM information symbol detection (through minimum mean square error criterion-based detection) at second stage. To combat the impact of smoothly time-varying environment, we emphasize on the utilization of ASF-ANN using backpropagation (BP) algorithm for the estimation of channel tap coefficients in frequency domain. The underlying ANN is modeled as feedforward multi-layered perceptron that updates the network weights. The major focus is on the gradient-descent algorithm-based adaptation of the slope of squashing-function (SF) along with other ANN parameters, which enhances the training efficiency of ASF-ANN in terms of the lower mean-squared channel estimation error in comparison with the traditional fixed-slope squashing-function (FSF) ANN technique. Simulation results corresponding to the underlying 4x1SFBC-OFDM system are presented to depict that ASF-ANN-based approach outperforms the FSF-ANN technique by providing lower bit-error-rate (BER) due to the usage of well-estimated CSI. At 15 dB SNR and fade rate = 0.001, the average BER reduces to 2.85x10(-4) for the ASF-ANN based approach, due to improved CSI estimation, which accounts for approximately 5% improvement in the detection success rate as compared to the FSF-ANN-based approach.

关键词： SFBC-OFDM Artificial neural networks (ANN) Adaptive-slope squashing-function Channel estimation Multi-layered perceptron back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of convolution neural network in medical image processing

引用

TECHNOLOGY AND HEALTH CARE 2021年第2期29卷 407-417页

作者： Liu, Jie Zhao, Hongbo Xian Med Univ Sch Basic Med Sci Xian Shaanxi Peoples R China Xian Med Univ Sch Med Technol Xian 710002 Shaanxi Peoples R China

backGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball "***" data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

关键词： Convolution neural network image processing self-learning back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

A Distributed MIMO Relay Scheme Inspired by backpropagation algorithm

A Distributed MIMO Relay Scheme Inspired by Backpropagation ...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Wang, Rui Jiang, Yi Zhang, Wei Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9781728181042

This paper studies a distributed scheme for a multi-input multi-output (MIMO) relay network, where the transmit nodes are subject to the nonlinear instantaneous power constraints. We introduce a novel perspective of regarding a relay network as a so-termed quasi-neural network by drawing its striking analogies with a (four-layer) artificial neural network (ANN). We propose a nonlinear amplify-and-forward (NAF) scheme inspired by the back-propagation (BP) algorithm, namely the NAF-BP, to optimize the transceivers to maximize the output signal-to-interference-plus-noise ratio (SINR) of the data streams. The NAF-BP algorithm can be implemented in a distributed manner with no channel state information (CSI) and no data exchange between the relay nodes. The NAF-BP can also coordinate the distributed relay nodes to form a virtual array to suppress interferences from unknown directions. Extensive simulations verify the effectiveness of the proposed scheme.

关键词： relay network quasi-neural network interference suppression back-propagation algorithm nonlinear amplify-and-forward

来源：评论

学校读者我要写书评

暂无评论

Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer 4

Advanced speed sensorless control strategy for induction mac...

引用

4th International Conference on Materials and Environmental Science (ICMES)

作者： El Merrassi, Weam Abounada, Abdelouahed Ramzi, Mohamed Univ Sultan Moulay Slimane Fac Sci & Technol Dept Elect Engn Lab Automat Energy Convers & Microelect LACEM BP 23000 Beni Mellal Morocco

High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Fourth edition of the International Conference on Materials & Environmental Science.

关键词： Artificial neural network (ANN) Model reference adaptive system (MRAS) Induction machine Speed sensorless control back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network

引用

MATERIALS 2020年第19期13卷 4266页

作者： Zazoum, Bouchaib Triki, Ennouri Bachri, Abdel Prince Mohammad Bin Fahd Univ Dept Mech Engn Al Khobar 31952 Saudi Arabia Coll Communautaire Nouveau Brunswick CCNB INNOV Caraquet NB E1W 1B6 Canada Southern Arkansas Univ Dept Phys & Engn Magnolia AR 71753 USA

Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters.

关键词： polymer clay nanocomposites mechanical properties deep neural network back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

An Efficient Quality Inspection of Food Products Using Neural Network Classification

引用

JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 1425-1440页

作者： Ali, Syed Sumera Ershad Dildar, Sayyad Ajij Dr Babasaheb Ambedkar Marathwada Univ Chhatrapati Shahu Maharaj Shikshan Sanstha Chh Shahu Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India MIT Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India

Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken division, feature extraction, and classification. At the first stage, the preprocessing method is used to remove all unnecessary noises. After that, modified region expansion-related segmentation is undertaken to segment the broken division of the food product. Then, feature extraction is used to remove the distinctive attributes of each food product to categorize their evaluation. Finally, the neural network classification procedure is used to examine the food quality. The proposed method is executed in the operational platform of MATLAB, and the consequences are examined by using obtainable methods.

关键词： Histogram equalization modified region growing segmentation color histogram features gray level co-variance matrix features artificial neural network classifier back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Matlab Implementation Of Multilayer Perceptron For Bearing Faults Classification

Matlab Implementation Of Multilayer Perceptron For Bearing F...

引用

作者： Doseděl, Martin Brno University of Technology

This paper deals with implementation of multilayer perceptron neural network (NN) forbearing faults classification. Neural network has been created from scratch as an M-script with backpropagation learning algorithm also, but without using advanced MATLAB packages. Public availablebearing dataset from CaseWestern Reserve University has been used for both training and testingphase, as well as for the final classification process. Problem with sparse input data for training thenetwork has also been addressed. This relatively simple and small neural network is capable to classifythe failures of a bearing with very low error rate.

关键词： Multilayer perceptron (MLP) deep learning data classification back-propagation algorithm bearing faults

来源：评论

学校读者我要写书评

暂无评论

PREDICTION OF THE ANTARCTIC MARGINAL ICE ZONE EXTENT BASED UPON ITS MULTIFRACTAL PROPERTY

引用

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2021年第2期29卷

作者： Liu, Yue Pang, Xiaoping Zhao, Xi Stein, Alfred Zhang, Xiang Ji, Qing Qu, Meng Dong, Jiang Wuhan Univ Chinese Antarctic Ctr Surveying & Mapping Wuhan 430079 Peoples R China Wuhan Univ Key Lab Polar Surveying & Mapping Natl Adm Surveying Mapping & Geoinformat Wuhan 430079 Peoples R China Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Tianjin Hydrog Ctr Nav Guarantee Ctr North China Sea Tianjin 300222 Peoples R China

The marginal ice zone (MIZ) around Antarctica is an important air-ice-ocean-wave interaction area and a crucial habitat for marine life. Given its dynamic nature, it is essential to understand and quantify both the short- and long-term changes of its extent. In this study, we investigated the MIZ extent time series using multifractal spectrum and R/S analysis, examining Antarctica as a whole and per sub-region. The MIZ extent was predicted using a back-propagation (BP) algorithm. The results show that the wide multifractal spectrum width of the MIZ extent time series has strong multifractal features for the entire Antarctic and for the five sub-regions. By comparing the spectrum width of the original time series with those of a shuffled series, we found that multifractality is related to long-range correlations in the time series. The Hurst exponent obtained using the R/S analysis indicates that the long-range correlation over six months is strongest for all time intervals. Compared to the autoregressive integrated moving average model, the mean absolute percentage error obtained using the BP algorithm was lower by 4-15%. We conclude that the BP algorithm combined with the multifractal property is well suited to predict the Antarctic MIZ extent.

关键词： Marginal Ice Zone Fractal Analysis Long-Range Correlation Prediction back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer

引用

Materials Today: Proceedings 2021年 45卷 7615-7621页

作者： Weam El Merrassi Abdelouahed Abounada Mohamed Ramzi Laboratory of Automatic Energy Conversion and Microelectronics (LACEM) Department of Electrical Engineering Faculty of Science and Technology University of Sultan Moulay Slimane Beni Mellal BP 23000. Morocco

关键词： Artificial neural network (ANN) Model reference adaptive system (MRAS) Induction machine Speed sensorless control back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：