Neural Network World最新文献_第6页

Automated Reverse Engineering of CAN Protocols CAN协议的自动逆向工程

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.015

Nils Weiss, Enrico Pozzobon, J. Mottok, V. Matousek

引用次数: 0

Independent EEG components are meaningful (for BCI based on motor imagery) 独立的脑电成分是有意义的(对于基于运动意象的脑机接口)

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.020

Yaroslav Kerechanin, P. Bobrov, A. Frolov, D. Húsek

{"title":"Independent EEG components are meaningful (for BCI based on motor imagery)","authors":"Yaroslav Kerechanin, P. Bobrov, A. Frolov, D. Húsek","doi":"10.14311/nnw.2021.31.020","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.020","url":null,"abstract":"Eight methods of decomposition of a multichannel EEG signal are compared in terms of their ability to identify the most physiologically significant components. The criterion for the meaningfulness of a method is its ability to reduce mutual information between components; to create components that can be attributed to the activity of dipoles located in the cerebral cortex; find components that are provided by other methods and for this case; and, at the same time, these components should most contribute to the accuracy of the BCI based on imaginary movement. Independent component analysis methods AMICA, RUNICA and FASTICA outperform others in the first three criteria and are second only to the common spatial patterns method in the fourth criterion. The components created by all methods for 386 experimental sessions of 27 subjects were combined into more than 100 clusters containing more than 10 elements. Additionally, the components of the 12 largest clusters were analyzed. They have proven to be of great importance in controlling BCI, their origins can be modeled using dipoles in the brain, and they have been detected by several degradation methods. Five of the 12 selected components have been identified and described in our previous articles. Even if the physiological and functional origins of the rest of identified components are to be the subject of further research, we have shown that their physiological nature is at least highly probable.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Tunnel displacement prediction under spatial effect based on Gaussian process regression optimized by differential evolution 空间效应下基于高斯过程回归的隧道位移预测

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.011

S. Zheng, A. Jiang, X. Yang

{"title":"Tunnel displacement prediction under spatial effect based on Gaussian process regression optimized by differential evolution","authors":"S. Zheng, A. Jiang, X. Yang","doi":"10.14311/nnw.2021.31.011","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.011","url":null,"abstract":"The prediction and analysis of surrounding rock deformation is a primary risk assessment method in tunnel engineering. However, the accurate prediction result is not easy to achieve due to the influence of multiple factors such as rock mass properties, support structure, and the spatial effect of tunnel construction. In this paper, a multivariate time-series model (MTSM) for tunnel displacement prediction is studied based on Gaussian process regression (GPR) optimized by differential evolutionary (DE) strategy, where the spatial effect is intuitively expressed through an extended time-series model. First, building learning samples for GPR, in which the inputs is the displacement data of the previous n days and the output is the data of the day (n + 1). Then, for each sample, an input item is added successively to form an expanded learning sample, which is the “distance between the construction face and monitoring section” on the day (n+ 1). Taking the root mean square error between the regression and measured data as the control index, the GPR model is trained to express the nonlinear mapping relationship between input and output, and the optimal parameters of this model are searched by DE. The displacement multivariate time-series model represented by DE-GPR is known as MTSM. On this basis, the applicability of GPR for tunnel displacement prediction and the necessity of DE optimization are illustrated by comparing the prediction results of several commonly used machine learning models. At the same time, the influence of GPR and DE parameters on the regression result and the computational efficiency of the MTSM model is analyzed, the recommendation for parameter values are given considering both calculation efficiency and accuracy. This method is successfully applied to the Leshanting tunnel of Puyan expressway in Fujian province, China, and the results show that the MTSM based on DE-GPR has a good ability in the deformation prediction of the surrounding rock, which provides a new method for tunnel engineering safety control.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Power purchase strategy of retail customers utilizing advanced classification methods 基于高级分类方法的零售客户购电策略

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.005

Lenka Jonáková, I. Nagy

引用次数: 3

Upper bounds on the node numbers of hidden layers in MLPs mlp中隐藏层节点数的上界

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.016

Jiang Liu, Feng Ni, Mingjun Du, Xuyang Zhang, Zhongli Que, Shihang Song

{"title":"Upper bounds on the node numbers of hidden layers in MLPs","authors":"Jiang Liu, Feng Ni, Mingjun Du, Xuyang Zhang, Zhongli Que, Shihang Song","doi":"10.14311/nnw.2021.31.016","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.016","url":null,"abstract":"It is one of the fundamental and challenging problems to determine the node numbers of hidden layers in neural networks. Various efforts have been made to study the relations between the approximation ability and the number of hidden nodes of some specific neural networks, such as single-hidden-layer and two-hiddenlayer feedforward neural networks with specific or conditional activation functions. However, for arbitrary feedforward neural networks, there are few theoretical results on such issues. This paper gives an upper bound on the node number of each hidden layer for the most general feedforward neural networks called multilayer perceptrons (MLP), from an algebraic point of view. First, we put forward the method of expansion linear spaces to investigate the algebraic structure and properties of the outputs of MLPs. Then it is proved that given k distinct training samples, for any MLP with k nodes in each hidden layer, if a certain optimization problem has solutions, the approximation error keeps invariant with adding nodes to hidden layers. Furthermore, it is shown that for any MLP whose activation function for the output layer is bounded on R, at most k hidden nodes in each hidden layer are needed to learn k training samples.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67124595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online centered NLMS algorithm for concept drift compensation 概念漂移补偿的在线中心NLMS算法

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.018

Matous Cejnek, J. Vrba

{"title":"Online centered NLMS algorithm for concept drift compensation","authors":"Matous Cejnek, J. Vrba","doi":"10.14311/nnw.2021.31.018","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.018","url":null,"abstract":"This paper introduces an online centered normalized least mean squares (OC-NLMS) algorithm for linear adaptive finite impulse response (FIR) filters and neural networks. As an extension of the normalized least mean squares (NLMS), the OC-NLMS algorithm features an approach of online input centering according to the introduced filter memory. This key feature can compensate the effect of concept drift in data streams, because such a centering makes the filter independent from the nonzero mean value of signal. This approach is beneficial for applications of adaptive filtering of data with offsets. Furthermore, it can be useful for real-time applications like data stream processing where it is impossible to normalize the measured data with respect to its unknown statistical attributes. The OC-NLMS approach holds superior performance in comparison to the NLMS for data with large offsets and dynamical ranges, due to its input centering feature that deals with the nonzero mean value of the input data. In this paper, the derivation of this algorithm is presented. Several simulation results with artificial and real data are also presented and analysed to demonstrate the capability of the proposed algorithm in comparison with NLMS.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67124971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Neural Network for the identification of a functional dependence using data preselection 神经网络识别的功能依赖使用数据预选

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.006

V. Hlavác

引用次数: 2

On the analysis of discrete data finding dependencies in small sample sizes 在小样本量的离散数据分析中寻找相关性

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2021-01-01 DOI: 10.14311/nnw.2021.31.017

S. Jozová, M. Matowicki, O. Přibyl, M. Zachová, Sathaporn Opasanon, R. Ziółkowski

引用次数: 2

Traffic accident risk classification using neural networks 基于神经网络的交通事故风险分类