检索结果-内蒙古大学图书馆

Performance comparison of neural network training algorithms in modeling of bimodal drug delivery

INTERNATIONAL JOURNAL OF PHARMACEUTICS 2006年第1-2期327卷 126-138页

作者： Ghaffari, A. Abdollahi, H. Khoshayand, M. R. Bozchalooi, I. Soltani Dadgar, A. Rafiee-Tehrani, M. IASBS Dept Chem Zanjan Iran Univ Tehran Med Sci Sch Pharm Tehran Iran Univ Ottawa Dept Mech Engn Ottawa ON K1N 6N5 Canada

The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal drug delivery. Artificial neural network (ANN) as a multilayer perceptron feedforward network was incorporated for developing a predictive model of the formulations. Five different training algorithms belonging to three classes: gradient descent, quasi-Newton (Levenberg-Marquardt, LM) and genetic algorithm (GA) were used to train ANN containing a single hidden layer of four nodes. The next objective of the current study was to compare the performance of aforementioned algorithms with regard to predicting ability. The ANNs were trained with those algorithms using the available experimental data as the training set. The divergence of the RMSE between the output and target values of test set was monitored and used as a criterion to stop training. Two versions of gradient descent backpropagation algorithms, i.e. incremental backpropagation (IBP) and batch backpropagation (BBP) outperformed the others. No significant differences were found between the predictive abilities of IBP and BBP, although, the convergence speed of BBP is three- to four-fold higher than IBP. Although, both gradient descent backpropagation and LM methodologies gave comparable results for the data modeling, training of ANNs with genetic algorithm was erratic. The precision of predictive ability was measured for each training algorithm and their performances were in the order of: IBP, BBP > LM > QP (quick propagation) > GA. According to BBP-ANN implementation, an increase in coating levels and a decrease in the amount of pectin-chitosan generally retarded the drug release. Moreover, the latter causal factor namely the amount of pectin-chitosan played slightly more dominant role in determination of the dissolution profiles. (c) 2006 Elsevier B.V. All rights reserved.

关键词： artificial neural network (ANN) bimodal drug delivery genetic algorithm (GA) gradient descent algorithm Levenberg-Marquardt (LM) algorithm modeling training algorithm

来源：评论

学校读者我要写书评

暂无评论

Linear trajectory models incorporating preprocessing parameters for speech recognition

引用

IEEE SIGNAL PROCESSING LETTERS 1998年第3期5卷 66-68页

作者： Chengalvarayan, R AT&T Bell Labs Lucent Technol Speech Proc Grp Naperville IL 60566 USA

In this letter, we investigate the interactions of front-end feature extraction and back-end classification techniques in nonstationary state hidden Markov model (NSHMM) based speech recognition. The proposed model aims at finding an optimal linear transformation on the mel-warped discrete Fourier tranform (DFT) features according to the minimum classification error (MCE) criterion. This linear transformation, along with the NSHMM parameters, are automatically trained using the gradient descent method. An error rate reduction of 8% is obtained on a standard 39-class TIMIT phone classification task in comparison with the MCE-trained NSHMM using conventional preprocessing techniques.

关键词： discrete cosine transform discriminative feature extraction gradient descent algorithm hidden Markov model mel-warped discrete Fourier transform

来源：评论

学校读者我要写书评

暂无评论

Towards Accurate Medical Image Segmentation With gradient-Optimized Dice Loss

引用

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 191-195页

作者： Ming, Qi Xiao, Xiaowu Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Liaoning Univ Sch Informat Resource Management Shenyang 110036 Peoples R China

Medical image segmentation plays an important role in medical diagnosis, and has received extensive attention in recent years. A large number of convolutional neural network based methods have been proposed to achieve accurate segmentation results. Dice loss is the most popular loss function for medical image segmentation tasks. However, we found that Dice loss suffers from abnormal gradient changes, which causes the loss function to be unstable and difficult to converge. Therefore, we propose an gradient-optimized Dice loss (GODC) to solve this problem. GODC corrects the abnormal gradient changes in the segmentation loss, which accelerates the model convergence and can achieve better segmentation performance. Next, we propose a lateral feature alignment module (LFAM). LFAM adopts deformable convolutional network to align the features of different layers on the shortcut connections of U-Net to improve the segmentation performance. Finally, our method achieves state-of-the-art results on the LiTS dataset as well as our collected pancreatic tumor datasets.

关键词： Medical image segmentation dice loss convolutional neural network gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of magnetic field near power lines by normalized radial basis function network

引用

ADVANCES IN ENGINEERING SOFTWARE 2011年第11期42卷 934-938页

作者： Rankovic, Vesna Radulovic, Jasna Univ Kragujevac Fac Mech Engn Kragujevac 34000 Serbia

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This work involved the computation of the magnetic field generated by 110 kV overhead power lines using a normalized radial basis function (NRBF) network. Training of the evolving NRBF network is achieved by using the data generated from the numerical simulation based on Charge Simulation method (CSM). Then, NRBF has been used to determine the magnetic field distribution in a new geometry differing from the geometries used for training. These test results show that proposed NRBF network can be used as useful tool to calculate the magnetic fields from power lines, alternative to the conventional methods. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Prediction Magnetic fields Power lines NRBF network Training set gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid binary logical regression -gradient decent approach for dynamic cooperative spectrum sensing in CRN

引用

INTERNATIONAL JOURNAL OF ELECTRONICS 2024年

作者： Supraja, C. Thandapani, Kavitha Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Elect & Commun Engn Chennai 600062 India

This paper proposes hybrid binary logical regression with a gradient decent optimisation (GDO) algorithm for spectrum sensing. From multiple secondary users (SU) systems, all signal vectors are collected with cognitive radios (CRs), and the features associated with the signal vector are considered for decision statistics. The decision statistics are modelled with a hybrid binary logical regression with gradient decent (BLR-GD) algorithm, which improves efficiency. The gradient decent (GD) is accomplished with binary logical regression (BLD), in which the regression coefficients are calculated efficiently. For evaluating the performance of the proposed algorithm, the spectrum sensing is evaluated in low signal-to-noise ratio (SNR) and high SNR scenarios. The detection probability, accuracy, F-measure and ROC curve performance of the proposed approach will outperform other existing approaches. The latency value of the proposed approach has reached 0.009 ms, which shows the efficiency of the proposed approach with 5 G communication features.

关键词： 5G technology cognitive radio network spectrum sensing binary logical regression gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

High-quality depth up-sampling via a supervised classification guided MRF model

引用

PATTERN RECOGNITION LETTERS 2020年 138卷 315-320页

作者： Qiao, Yiguo Jiao, Licheng Tang, Xu Li, Wenbin Cosker, Darren Xidian Univ Int Res Ctr Intelligent Percept & Computat Sch Artificial IntelligenceMinist EducJoint Int Key Lab Intelligent Percept & Image Understanding 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Univ Bath Ctr Anal Mot Entertainment Res & Applicat CAMERA Bath BA2 7AY Avon England

In this paper, a Supervised Classification assisted Markov Random Field (SC-MRF) model is proposed for generating high-quality up-sampled depth maps. The proposed model aims to reduce depth bleeding and depth confusion artifacts that can be produced at boundary regions of the up-sampled depth maps. In the proposed model, segmentation of low-resolution (LR) depth map is first used to supervise the classification of corresponding high-resolution (HR) color image. With this supervised classification, not only can the depth edges be retained, but redundant textures in the HR color image can be omitted. The classification result is then introduced into the design of a MRF energy function, and the final up-sampled depth map is obtained by optimizing this energy function with the gradient descent algorithm. For simplicity, classical K-means clustering is adopted to segment the LR depth map into several classes, and a feature-based K-nearest neighbour (K-NN) method is utilized for the supervised classification. With the proposed SC-MRF model, interaction between depths of different classes will be strongly suppressed, meaning depth edges are well preserved. Comparisons with the state-of-the-art demonstrate the strong performance of the proposed method both visually and by quantitative evaluation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Markov random fields K-means clustering K-NN Supervised classification gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition

引用

IET BIOMETRICS 2014年第4期3卷 278-290页

作者： Wang, Faqiang Zhang, Hongzhi Wang, Kuanquan Zuo, Wangmeng Harbin Inst Technol Computat Percept & Cognit Ctr Sch Comp Sci & Technol Harbin 150001 Peoples R China

For the robust recognition of noisy face images, in this study, the authors improved the fast neighbourhood component analysis (FNCA) model by introducing a novel spatially smooth regulariser (SSR), resulting in the FNCA-SSR model. The SSR can enforce local spatial smoothness by penalising large differences between adjacent pixels, and makes FNCA-SSR model robust against noise in face image. Moreover, the gradient of SSR can be efficiently computed in image space, and thus the optimisation problem of FNCA-SSR can be conveniently solved by using the gradient descent algorithm. Experimental results on several face data sets show that, for the recognition of noisy face images, FNCA-SSR is robust against Gaussian noise and salt and pepper noise, and can achieve much higher recognition accuracy than FNCA and other competing methods.

关键词： face recognition gradient methods statistical analysis fast neighbourhood component analysis spatially smooth regulariser robust noisy face recognition FNCA-SSR model local spatial smoothness adjacent pixels optimisation problem gradient descent algorithm face data sets Gaussian noise salt and pepper noise

来源：评论

学校读者我要写书评

暂无评论

Fuzzy on-line identification of SISO nonlinear systems

引用

FUZZY SETS AND SYSTEMS 1999年第3期107卷 323-334页

作者： Xie, WF Rad, AB Hong Kong Polytech Univ Dept Elect Engn Hung Hom Kowloon Peoples R China

A new fuzzy on-line identification algorithm for a single input/single output continuous-time nonlinear dynamic system is presented. This method combines the conventional on-line identification with fuzzy logic system. The nonlinear system is approximated by a set of fuzzy rules that describe the local linear dynamic in each subspace formed by fuzzifying the input and output space. The continuous-time fuzzy input-output model is identified on-line by using the input and output measurements. A fuzzy identification algorithm has been developed and a convergence analysis is carried out. Simulation studies have demonstrated that this fuzzy on-line identifier can match the time-varying nonlinear system within +/-5% accuracy. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： fuzzy model on-line identification gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Listwise approach based on the cross-correntropy for learning to rank

引用

ELECTRONICS LETTERS 2018年第14期54卷 878-879页

作者： Wu, Mintao Zhu, Jihua Wang, Jun Pang, Shanmin Li, Yaochen Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Jiangnan Univ Sch Digital Media Wuxi 214122 Peoples R China

The problem of learning to rank is addressed and a novel listwise approach by taking document retrieval as an example is proposed. It first introduces the concept of cross-correntropy into learning to rank and then proposes the listwise loss function based on the cross-correntropy between the ranking list given by the label and the one predicted by training model. The use of the cross-correntropy loss leads to the development of the listwise approach called ListCCE, which employs the gradient descent algorithm to train a neural network model. Experimental results tested on publicly available data sets show that the proposed approach performs better than some existing approaches.

关键词： information retrieval learning (artificial intelligence) entropy gradient methods listwise approach cross-correntropy loss learning to rank document retrieval listwise loss function training model ListCCE gradient descent algorithm neural network model

来源：评论

学校读者我要写书评

暂无评论

A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG

引用

NEURAL COMPUTING & APPLICATIONS 2006年第1期15卷 49-54页

作者： Acir, N Nigde Univ Dept Elect & Elect Engn TR-51245 Nigde Turkey

In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.

关键词： gradient descent algorithm feature extraction asymmetric basis functions electroencephalography

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：