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[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering最新文献

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An overview of the CMAC neural network CMAC神经网络概述
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163366
F. Glanz, W. Miller, L. Kraft
{"title":"An overview of the CMAC neural network","authors":"F. Glanz, W. Miller, L. Kraft","doi":"10.1109/ICNN.1991.163366","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163366","url":null,"abstract":"The authors describe the cerebellar model arithmetic computer (CMAC) neural network, which is an alternative to backpropagated multilayer networks. CMAC has properties of generalization, rapid algorithmic computation based on least-mean-square (LMS) training, functional representation, output superposition, and practical hardware realization, all of which are discussed. Data concerning CMAC capacity and generalization are shown. Brief descriptions of applications in pattern recognition, robot control, and signal processing are given.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126802466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 73
Design of an intelligent control system for remotely operated vehicles 遥控车辆智能控制系统的设计
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163341
J. Yuh, R. Lakshmi
{"title":"Design of an intelligent control system for remotely operated vehicles","authors":"J. Yuh, R. Lakshmi","doi":"10.1109/ICNN.1991.163341","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163341","url":null,"abstract":"The application of a neural network controller is described. Three learning algorithms for online implementation of the controller are discussed. These control schemes do not require any information about the system dynamics except an upper bound of the inertia terms. Selection of the number of layers in the neural network, the number of neurons in the hidden layer, initial weights for the network, and the critic coefficient was done based on the results of preliminary tests. The performances of the three learning algorithms were compared. The effectiveness of the neural net controller in handling time-varying parameters and random noise was tested by a case study on a remotely operated vehicle (ROV) system for robotic underwater operations. The results of the comparisons and the testing are presented in detail.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116657975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Calibrating the performance of neural networks 校准神经网络的性能
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163372
R. Barton, D. B. Fogel, A. Krieger
{"title":"Calibrating the performance of neural networks","authors":"R. Barton, D. B. Fogel, A. Krieger","doi":"10.1109/ICNN.1991.163372","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163372","url":null,"abstract":"The authors offer a procedure to assess the performance of signal classifiers, including neural networks. An example is described wherein three neural classifiers are tested in their ability to discriminate between a modeled underwater man-made event, real clutter signals, and a modeled quiet ocean background. Empirical studies using neural classifiers on ocean acoustic data are described. Some observations regarding the utility of the outline procedure are offered.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123619396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
ARACHYDE: a sensor-to-situation assessment software architecture for passive acoustic signal understanding ARACHYDE:一种用于被动声信号理解的传感器-态势评估软件架构
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163359
T. Lefebvre, A. Lemer, F. Dispot
{"title":"ARACHYDE: a sensor-to-situation assessment software architecture for passive acoustic signal understanding","authors":"T. Lefebvre, A. Lemer, F. Dispot","doi":"10.1109/ICNN.1991.163359","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163359","url":null,"abstract":"In antisubmarine warfare (ASW) acoustic signal processing, of sonar signals, four main tasks must be achieved: detection, localization, classification and interpretation. The authors focus on how a hybrid software technology (signal processing+neural networks+knowledge-based systems) can help to achieve the interpretation task and its related reaction to detection and classification. A SUN4-based prototype called ARACHYDE has been designed to test this approach on actual acoustic situation analysis. Its goal is to establish an online acoustic situation assessment. An application to the sounds recorded in a quiet office is described.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Classification of euceratium gran. in neural networks 木樨草的分类。在神经网络中
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163354
R. Simpson, P. Culverhouse, R. Ellis, B. Williams
{"title":"Classification of euceratium gran. in neural networks","authors":"R. Simpson, P. Culverhouse, R. Ellis, B. Williams","doi":"10.1109/ICNN.1991.163354","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163354","url":null,"abstract":"Two species of Euceratium, Ceratium arcticum (Ehrb.) and C. longipes (Bail.), were selected in an attempt to automatically distinguish one species from another. A backward error-propagation neural network model was given data obtained from processing images of specimen Ceratium, trained to respond correctly to that data, and was subsequently found to have a significant ability to classify new (unseen) data. This ability to learn a set of training data shows that the network was able to formulate an internal representation that distinguished between the two species. Furthermore, that the network was able to correctly classify new data indicates that the representation so formed is sufficient to distinguish the species in general. It is suggested that the application of neural networks to this domain will enable automatic classification of species from images.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117011069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
A characterization of neural network performances based on Fokker-Planck statistical models 基于Fokker-Planck统计模型的神经网络性能表征
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163373
D. Colella, P. Hriljac, G. Jacyna
{"title":"A characterization of neural network performances based on Fokker-Planck statistical models","authors":"D. Colella, P. Hriljac, G. Jacyna","doi":"10.1109/ICNN.1991.163373","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163373","url":null,"abstract":"The authors examine the connection between training period and detection performance by showing that a network can be described by a Fokker-Planck statistical model. Closed-form expressions are derived for the weight probabilities under suitable assumptions on the weight adaptivity and the noise process. Output node statistics are determined by computing the conditional output density as a function of the input statistics and averaging over the weight probabilities for a specific training time. It is shown that the training period is dominated by the time required to stabilize the bias weight. This weight is analogous to an adaptive threshold and is related directly to the network false alarm probability. A second issue addressed is the steady-state performance of the network. Explicit expressions are derived for the false alarm and detection probabilities. The authors show that the network implements a classical mini-max best.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"111 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126256925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Experimental comparison between neural networks and classical techniques of classification applied to natural underwater transients identification 神经网络与经典分类技术在自然水下瞬态识别中的实验比较
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163335
D. Legitimus, L. Schwab
{"title":"Experimental comparison between neural networks and classical techniques of classification applied to natural underwater transients identification","authors":"D. Legitimus, L. Schwab","doi":"10.1109/ICNN.1991.163335","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163335","url":null,"abstract":"The authors present an application of the joint use of signal processing techniques and neural networks to identify transient natural underwater sounds. The work focused on sounds of very short duration (typically 5 to 50 ms). Each context-free click is described by a reduced set of 31 input parameters, by the use of the autoregressive modeling and the Daubechies wavelets transform. The performances obtained by the Adaline-like-network (ALN) and the multilayered perceptron (MLP), and those obtained by classical techniques of classification (factorial discriminant analysis, and a clustering algorithm) are compared. A dichotomic approach and a multiclass approach were used.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131303161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Neural networks and the classification of complex sonar signals 神经网络与复杂声纳信号的分类
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163363
R. Gorman
{"title":"Neural networks and the classification of complex sonar signals","authors":"R. Gorman","doi":"10.1109/ICNN.1991.163363","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163363","url":null,"abstract":"The author examines the ability of neural networks to extract high-order information from a set of input patterns, and relates this ability to advantages over previous approaches to both active and passive sonar signal classification. The basic computational structure of feedforward neural networks is reviewed and the ability of these networks to extract high-order information from various signals is examined. The hierarchical neural network, is examined as an alternate means of extracting information from highly structured non-Gaussian sonar signals. The promise of dynamic neural networks as an approach to the classification of complex sonar signals is discussed.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Evolutionary methods for training neural networks 神经网络训练的进化方法
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163368
D. B. Fogel, L. Fogel, V. W. Porto
{"title":"Evolutionary methods for training neural networks","authors":"D. B. Fogel, L. Fogel, V. W. Porto","doi":"10.1109/ICNN.1991.163368","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163368","url":null,"abstract":"Training neural networks by the implementation of a gradient-based optimization algorithm (e.g., back-propagation) often leads to locally optimal solutions which may be far removed from the global optimum. Evolutionary optimization methods offer a procedure to stochastically search for suitable weights and bias terms given a specific network topology. The topics discussed are evolutionary programming; genetic algorithms; evolutionary function optimization experiments; background to classification problems and experimental results with evolutionary training.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Acoustic transient analysis using wavelet decomposition 基于小波分解的声瞬态分析
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering Pub Date : 1991-08-15 DOI: 10.1109/ICNN.1991.163324
M. Desai, D.J. Shazeer
{"title":"Acoustic transient analysis using wavelet decomposition","authors":"M. Desai, D.J. Shazeer","doi":"10.1109/ICNN.1991.163324","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163324","url":null,"abstract":"The authors demonstrate the use of wavelet decomposition in extracting relevant information from passive acoustic signals. These decompositions were used in generating features for classifiers which were applied against the standard data set of transients obtained from NUSC. Complete separation of four classes, i.e., three transients and a quiet ocean background, was obtained using two classification approaches: one based on a quadratic Bayesian classifier and the other based on a multilayer perceptron. The authors describe the wavelet-based features and the classifier design and provide class scatter diagrams.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128424171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 26
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