检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Data Stream Mining and Processing (DSMP)

作者： Aizenberg, Igor Khaliq, Zain Manhattan Coll Riverdale NY 10471 USA

ISBN: (纸本)9781538628744

There is a wealth of analysis techniques that can be used in analyzing data of such a nature as EEG (Electroencephalogram), yet there are still many more ways and possibilities of analysis techniques to consider in order to produce a method that far exceeds the capabilities of the prevalent method. Since a multilayer neural network with multi-valued neurons (MLMVN) was successfully used earlier to decode EEG signals in a brain/computer interface (BCI) by analysis of their Fourier transform, it seemed very attractive to use it as a tool for EEG analysis. This work aims to further investigate how a complex-valued machine learning tool can be used to analyze EEG in the frequency domain. Our goal was to check how Fourier transform and complex wavelet transform of EEG can be analyzed using MLMVN in order to diagnose epilepsy, its remission or absence. We worked with a commonly used benchmark data set of epilepsy-related EEGs. The analysis of the transformed data was performed to determine a set of relevant statistical characteristics of DTCWT and Fourier transform components, which were then used as inputs of the MLMVN. The obtained results show a very high efficiency of the proposed approach.

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN EEG Fourier transform

来源：评论

学校读者我要写书评

暂无评论

GPU Simulator of multilayer neural network Based on multi-valued neurons

GPU Simulator of Multilayer Neural Network Based on Multi-Va...

引用

International Joint Conference on neural networks (IJCNN)

作者： Hacker, Christian Aizenberg, Igor Wilson, Jeff Texas A&M Univ Texarkana Texarkana TX 75503 USA Manhattan Coll Riverdale NY USA

ISBN: (纸本)9781509006199

In this paper, we consider principles of design and basic fundamentals of a GPU simulator of the multilayer neural network with multi-valued neurons (MLMVN). Slowing down a learning process due to a big learning dataset and/or a big neural network needed for solving a certain problem is a potential bottleneck preventing the use of neural networks for solving some challenging problems. The same is related to deep learning. MLMVN is a feedforward complex-valued neural network, which has a number of advantages when compared to real-valued neural networks. These advantages include derivative-free learning and significantly better generalization capability. To extend applicability of MLMVN, its GPU-based software implementation shall be considered. We present basic principles of the GPU simulator of MLMVN and how matrix algebra operations are specifically employed there. It is shown that the bigger the network is, the more beneficial is its GPU implementation. It is shown that up to 32x acceleration can be achieved for the MLMVN learning process. Some applications, which could not be even considered without a GPU simulator, are also presented.

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN Intelligent Filtering

来源：评论

学校读者我要写书评

暂无评论

GPU Simulator of multilayer neural network Based on multi-valued neurons

GPU Simulator of Multilayer Neural Network Based on Multi-Va...

引用

International Joint Conference on neural networks

作者： Christian Hacker Igor Aizenberg Jeff Wilson Texas A&M University-Texarkana Manhattan College

ISBN: (纸本)9781509006212

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN Intelligent Filtering multilayer neural networks GRAPPER PICK UP Graphics Processing Unit neural network simulator design theory Learning processes software implementation

来源：评论

学校读者我要写书评

暂无评论

System Identification using FRA and a modified MLMVN with Arbitrary Complex-valued Inputs

System Identification using FRA and a modified MLMVN with Ar...

引用

International Joint Conference on neural networks (IJCNN)

作者： Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina Manhattan Coll Riverdale NY 10471 USA Univ Florence Dept Informat Engn Florence Italy

ISBN: (纸本)9781509006199

A new architecture for the identification of lumped models of distributed parameter systems is proposed in this paper. It is assumed to use, by means of repeated measurements or intensive simulations, the Frequency Response Analysis (FRA) of the system or device under exam. The procedure can be used to extract the component values. A key element of this architecture is a multilayer neural network with multi-valued neurons (MLMVN), which is modified in the paper in order to use arbitrary complex-valued inputs. It is shown that this modification requires just a slight change in the MLMVN learning algorithm.

关键词： Lumped Models multi-valued Neuron multilayer neural network with multi-valued neurons Analog Circuits Testability

来源：评论

学校读者我要写书评

暂无评论

MLMVN as an Intelligent Image Filter

MLMVN as an Intelligent Image Filter

引用

International Joint Conference on neural networks (IJCNN)

作者： Aizenberg, Igor Ordukhanov, Alan O'Boy, Fionntan Manhattan Coll Riverdale NY 10471 USA

ISBN: (纸本)9781509061822

In this paper, we employ the multilayer neural network with multi-valued neurons (MLMVN) as an intelligent image filter. MLMVN is a powerful complex-valued feedforward neural network, which has shown its high efficiency in solving various classification and prediction problems. Here we employ it as an intelligent filter. MLMVN containing a single hidden layer is used to filter overlapping patches taken from a noisy image. Then the resulting image is obtained by averaging the intensities in all overlapping pixels. MLMVN is trained using a number of n x n patches from different images to obtain a clean patch from a noisy one. Then MLMVN is used to process overlapping patches taken from a noisy image. It is shown that this approach works better when an m x m patch is resulted from an n x n patch where m < n, thus when the inner area of the patch is filtered. It is shown that this filter is robust, since it performs well on different images, which did not participate in the learning process. It terms of PSNR, this approach shows results better or comparable with BM3D filter commonly recognized as the best nonlinear filter. A specific advantage of the presented approach is its ability to preserve small details carefully. A batch learning algorithm (MLMVN-LLS) is employed to train the network.

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN Intelligent Filtering Nonlinear Filtering

来源：评论

学校读者我要写书评

暂无评论

Impulse Noise Filtering using MLMVN

Impulse Noise Filtering using MLMVN

引用

International Joint Conference on neural networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Keohane, Olivia Aizenberg, Igor Manhattan Coll Riverdale NY 10471 USA

ISBN: (纸本)9781728169262

In this paper, we consider how a complex-valued neural network, the multilayer neural network with multi-valued neurons (MLMVN), can be efficiently used for impulse noise filtering. It is shown that MLMVN with only a single hidden layer of neurons can restore an image corrupted by random valued impulse noise while carefully preserving edges and boundaries. MLMVN processes overlapping patches, which to a noisy image should be broken down. A network shall be trained using a robust learning set created from patches randomly picked up from many images. Then, the trained network should be used for actual filtering. Final intensities in pixels of a resulting image are obtained by averaging the intensities over all overlapping patches. This approach becomes highly efficient for images corrupted by random impulse noise with a low corruption rate of 5-10%. It makes it possible to preserve the smallest image details very carefully and avoid the smoothing of edges. MLMVN outperforms sophisticated filters with detectors of impulse noise for a lower corruption rate and shows comparable results for a higher corruption rate.

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN Intelligent Filtering Impulse noise

来源：评论

学校读者我要写书评

暂无评论

Image Recognition using MLMVN and Frequency Domain Features

Image Recognition using MLMVN and Frequency Domain Features

引用

International Joint Conference on neural networks (IJCNN)

作者： Aizenberg, Igor Gonzalez, Alexander Manhattan Coll Riverdale NY 10471 USA

ISBN: (纸本)9781509060146

In this paper, we develop a new approach to image recognition. This approach is based on the analysis of frequency domain features (namely Fourier transform phases corresponding to certain frequencies) using the multilayer neural network with multi-valued neurons (MLMVN). MLMVN is a powerful complex-valued feedforward neural network, which has shown its high efficiency in solving various classification, prediction, and intelligent image filtering problems. As a complex-valued neural network, MLMVN has an ability to treat the phase information properly, completely preserving a circular nature of phase. At the same time it is known that phases contain all information about image edges, their location and spatial orientation. This means that the phase information can be used for image recognition. Particularly, this is the case when it is necessary to recognize objects whose size is known and fixed and it is possible to use phases corresponding to certain frequencies found based on the Nyquist-Shannon theorem as features for recognition. These phases can then be analyzed using MLMVN. We illustrate this approach using the famous MNIST image dataset, which for a 100% recognition rate was achieved. Phases corresponding only to the three lowest frequencies (1 to 3) and just a single hidden layer MLMVN are enough to achieve this result. A batch learning algorithm (MLMVN-SM-LLS) is employed to train the network.

关键词： Complex-valued neural networks multi-valued Neuron multilayer neural network with multi-valued neurons MLMVN Image Recognition MNIST Frequency Domain Fourier Transform Phase

来源：评论

学校读者我要写书评

暂无评论

Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

引用

FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN multilayer feed-forward neural networks MLP multilayer perceptron BP Back Propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN Back propagation artificial neural network NN neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA Gravitational search algorithm PSO Particle swarm optimization MPA Marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA Bayesian regularization algorithm MNN multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF Random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

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：