检索结果-内蒙古大学图书馆

PROTEIN FOLD CLASSIFICATION BASED ON MACHINE learning PARADIGM - A REVIEW

BIO-algorithmS AND MED-SYSTEMS 2012年第1期8卷 53-76页

作者： Stapor, Katarzyna Silesian Tech Univ Inst Comp Sci Akad 16 PL-44100 Gliwice Poland

Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.

关键词： supervised learning algorithm classifier features protein fold recognition

来源：评论

学校读者我要写书评

暂无评论

supervised learning in a Single Layer Dynamic Synapses Neural Network

Supervised Learning in a Single Layer Dynamic Synapses Neura...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yousefi, Ali Dibazar, Alireza A. Berger, Theodore W. Univ So Calif Neural Dynam Lab Los Angeles CA 90089 USA

ISBN: (纸本)9781424496365

The main focus of this paper is to introduce a new supervised learning algorithm for spiking neural networks. The learning algorithm minimizes the overall differences between spike times of target and test spike trains by utilizing a new quantitative similarity measure which has been defined in this work. The actual membrane potential of a post-synaptic neuron is adjusted at the time of spikes based on what has been measured from similarity measure in order to generate the desired membrane potential. Finally, by utilizing gradient descent algorithm, the parameters of the spiking neural network are tuned to generate the desired output membrane potential. The proposed algorithm was applied to tune the facilitation, depression, and synaptic weight constants of the Dynamic Synapses Neural Network - DSNN - for the aim of input-output functional mapping. The simulation results show that the system identification task converges to the global optimum. The rate-to-time coding simulation performs with more than 75 percent accuracy. The performance of both system identification and rate-to-time coding is due to adaptation of short and long term synaptic parameters which cannot be accomplished if only synaptic weight is adapted.

关键词： Adaptation models Calcium Energy states Filtering Heuristic algorithms Mathematical model Neurons depression weight constant facilitation weight constant gradient descent algorithm gradient methods input-output functional mapping learning (artificial intelligence) neural nets post-synaptic neuron rate-to-time coding simulation single layer dynamic synapses neural network spike train supervised learning algorithm synaptic weight constant

来源：评论

学校读者我要写书评

暂无评论

An Optimal Construction and Training of Second Order RBF Network for Approximation and Illumination invariant Image Segmentation

An Optimal Construction and Training of Second Order RBF Net...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Cai, Xun Tyagi, Kanishka Manry, Michael T. Shandong Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China Univ Texas Arlington Dept Elect Engn Arlington 76010 TX USA

ISBN: (纸本)9781424496365

In this paper, we proposed an hybrid optimal radial-basis function (RBF) neural network for approximation and illumination invariant image segmentation. Unlike other RBF learning algorithms, the proposed paradigm introduces a new way to train RBF models by using optimal learning factors (OLFs) to train the network parameters, i.e. spread parameter, kernel vector and a weighted distance measure (DM) factor to calculate the activation function. An efficient second order Newton's algorithm is proposed for obtaining multiple OLF's (MOLF) for the network parameters. The weights connected to the output layer are trained by a supervised-learning algorithm based on orthogonal least square (OLS). The error obtained is then back-propagated to tune the RBF parameters. By applying RBF network for approximation on some real-life datasets and classification to reduce illumination effects of image segmentation, the results show that the proposed RBF neural network has fast convergence rates combining with low computational time cost, allowing it a good choice for real-life application such as image segmentation.

关键词： Classification algorithms Delta modulation Image color analysis Image segmentation Newton method RBF learning algorithm RBF network training Radial basis function networks Support vector machine classification Training activation function backpropagation backpropagation hybrid optimal radial-basis function neural network illumination effect reduction illumination invariant image segmentation image classification image classification image segmentation kernel vector least squares approximations network parameter training optimal RBF network construction optimal learning factors orthogonal least square radial basis function networks second order Newton algorithm second order RBF network spread parameter supervised learning algorithm weighted distance measure factor

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Sound Localisation Techniques using Cross-Correlation and Spiking Neural Networks for Mobile Robotics

A Comparison of Sound Localisation Techniques using Cross-Co...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wall, Julie A. McGinnity, Thomas M. Maguire, Liam P. Univ Ulster Intelligent Syst Res Ctr Sch Comp & Intelligent Syst Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9781424496365

This paper outlines the development of a cross-correlation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

关键词： Correlation Delay Delay lines Ear Microphones Neurons Robots bushy cells cochlear nucleus control engineering computing correlation methods cross-correlation algorithm encoding encoding layer interaural time difference learning (artificial intelligence) mammalian auditory pathway medial superior olive mobile robotics mobile robots neural nets sound localisation ability simulation sound localisation technique spike trains spiking neural network supervised learning algorithm

来源：评论

学校读者我要写书评

暂无评论

A Training algorithm for SpikeProp Improving Stability of learning Process

A Training Algorithm for SpikeProp Improving Stability of Le...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wakamatsu, Toshiki Takase, Haruhiko Kawanaka, Hiroharu Tsuruoka, Shinji Mie Univ Grad Sch Engn Tsu Mie 514 Japan

ISBN: (纸本)9781424496365

In this paper, we aims to improve stability of learning processes by the SpikeProp algorithm. We proposed the method that reduce the increase of the error in learning processes. It repeats two steps: (1) original SpikeProp algorithm, and (2) use a linear search in the steepest descent direction only if the first step is failed. Some experimental results shows the improvement of learning processes.

关键词： Biological neural networks Iris Iris recognition Mathematical model Neurons SpikeProp algorithm SpikeProp network Surges Training feed-forward networks feedforward neural nets learning (artificial intelligence) learning process spiking neural networks stability stability improvement supervised learning algorithm training algorithm

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Neuro-Fuzzy Architecture for Intelligent Control of a Servo System and its Experimental Evaluation

An Adaptive Neuro-Fuzzy Architecture for Intelligent Control...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Aras, Ayse Cisel Kayacan, Erdal Oniz, Yesim Kaynak, Okyay Abiyev, Rahib Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkey Near East Univ Dept Comp Engn Nicosia Cyprus

ISBN: (纸本)9781424463916

In this paper the development of an adaptive neuro-fuzzy architecture for the speed control of a servo system with nonlinear load is presented. The synthesis of the structure is described and a learning algorithm for the neuro-fuzzy control system is derived. The supervised learning algorithm is used to train the unknown coefficients of the system, and then the fuzzy rules of the neuro-fuzzy system are generated. A number of simulation studies are carried out, and the results are compared with those obtained with a PI controller tuned using desired time response characteristics. These and the experimental studies presented show that the neuro-fuzzy control system has a better control performance than the conventional PI controller.

关键词： DC motor PI control PI control adaptive control adaptive neuro-fuzzy architecture control system synthesis fuzzy neural nets fuzzy rules intelligent control intelligent control learning (artificial intelligence) neurocontrollers servo system servomechanisms speed control supervised learning algorithm velocity control

来源：评论

学校读者我要写书评

暂无评论

An Experimental Research of Traffic Identification algorithms in Broadband Network

An Experimental Research of Traffic Identification Algorithm...

引用

1st International Symposium on Computer Network and Multimedia Technology

作者： Chen, Na Shou, Guochu Hu, YiHong Guo, ZhiGang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100088 Peoples R China

ISBN: (纸本)9781424452729

Adopting the machine learning method to identify the traffic in the broadband network has become a new research direction in this field. In this paper, we evaluate 11 kinds of supervised learning algorithm of the machine learning through the index of testing time, modeling time, accuracy rate and CPU utilization ratio, then select suitable algorithms for the broadband network. Through the analysis we draw the conclusion that the algorithms of C4.5, RandomTree. OneR and BayesNet fit the traffic identification in the broadband network.

关键词： traffic identification machine learning supervised learning algorithm broadband network

来源：评论

学校读者我要写书评

暂无评论

Adaptive nonlinear equalizer with reduced computational complexity

引用

SIGNAL PROCESSING 1995年第3期47卷 307-317页

作者： Oh, DG Choi, JY Lee, CW Satellite Broadcasting System Section Electronics and Telecommunications Research Institute P. O. Box 106 Taejon 305-306 South Korea School of Electrical Engineering Seoul National University Seoul South Korea

This paper presents an approach of adaptive nonlinear equalization having much less computational complexity than existing nonlinear equalizers. The proposed equalizer first estimates many local regions in the symbol decision space, and then performs a biased finite impulse response filtering in each estimated local region, The final estimate of the transmitted symbol is obtained by a weighted average of the local results, By using a self-organizing and localized linear tuning methods, the proposed nonlinear equalizer can achieve an almost optimal decision making capability like the existing nonlinear equalizers, Simulation results show that the proposed equalizer can be effectively applied to on-line adaptation environments due to its reduced computational complexity, making complexity similar to that of a linear equalizer.

关键词： adaptive nonlinear equalizers self-organizing method supervised learning algorithm weighted average biased finite impulse response filter

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：