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[Proceedings 1992] IJCNN International Joint Conference on Neural Networks最新文献

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Modeling neural network dynamics using iterative image reconstruction algorithms 使用迭代图像重建算法建模神经网络动力学
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.227312
R. Steriti, M. Fiddy
{"title":"Modeling neural network dynamics using iterative image reconstruction algorithms","authors":"R. Steriti, M. Fiddy","doi":"10.1109/IJCNN.1992.227312","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.227312","url":null,"abstract":"Image reconstruction problems can be viewed as energy minimization problems and can be mapped onto a Hopfield neural network. For image reconstruction problems the authors describe the Gerchberg-Papoulis iterative method and the priorized discrete Fourier transform (PDFT) algorithm (C.L. Byrne et al., 1983). Both of these can be mapped onto a Hopfield neural network architecture, with the PDFT incorporating an iterative matrix inversion. The equations describing the operation of the Hopfield neural network are formally equivalent to those used in these iterative reconstruction methods, and these iterative reconstruction algorithms are regularized. The PDFT algorithm is a closed form solution to the Gerchberg-Papoulis algorithm when image support information is used. The regularized Gerchberg-Papoulis algorithm can be implemented synchronously, from which it follows that the Hopfield neural network implementation can also converge.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126752169","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: 0
Learning fuzzy rule-based neural networks for function approximation 学习基于模糊规则的神经网络的函数逼近
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.287127
C. Higgins, R. M. Goodman
{"title":"Learning fuzzy rule-based neural networks for function approximation","authors":"C. Higgins, R. M. Goodman","doi":"10.1109/IJCNN.1992.287127","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.287127","url":null,"abstract":"The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114914642","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: 33
A neural network architecture for load forecasting 一种用于负荷预测的神经网络结构
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.226948
H. Bacha, W. Meyer
{"title":"A neural network architecture for load forecasting","authors":"H. Bacha, W. Meyer","doi":"10.1109/IJCNN.1992.226948","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.226948","url":null,"abstract":"Neural networks offer superior performance for predicting the future behaviour of pseudo-random time series. The authors present a neural network architecture for load forecasting which is capable of capturing the relevant relationships and weather trends. The proposed architecture is tested by training three neural networks, which in turn are tested with weather data form the same four-day period. The network is made up of a series of subnetworks each connected to its immediate neighbors in a way that takes into consideration not only current weather conditions but also the weather trend around the hour for which the forecast is being made. The neural network forecasts were very close to the actual values despite the facts that only a small sample was used and there were errors in the data. A more comprehensive study is being contemplated for the next phase. One of the issues to be addressed is the expansion of the scope of the research to include data from a complete season (three consecutive months) over several years.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116147726","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: 30
Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem 多变量飞机控制问题鲁棒动态神经控制器的设计与评价
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.287193
T. Troudet, Sanjay Garg, Walter C. Merrill
{"title":"Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem","authors":"T. Troudet, Sanjay Garg, Walter C. Merrill","doi":"10.1109/IJCNN.1992.287193","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.287193","url":null,"abstract":"The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116585334","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: 11
Handwritten alpha-numeric recognition by a self-growing neural network 'CombNET-II' 自生长神经网络CombNET-II的手写字母数字识别
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.227337
A. Iwata, Y. Suwa, Y. Ino, N. Suzumura
{"title":"Handwritten alpha-numeric recognition by a self-growing neural network 'CombNET-II'","authors":"A. Iwata, Y. Suwa, Y. Ino, N. Suzumura","doi":"10.1109/IJCNN.1992.227337","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.227337","url":null,"abstract":"CombNET-II is a self-growing four-layer neural network model which has a comb structure. The first layer constitutes a stem network which quantizes an input feature vector space into several subspaces and the following 2-4 layers constitute branch network modules which classify input data in each sub-space into specified categories. CombNET-II uses a self-growing neural network learning procedure, for training the stem network. Back propagation is utilized to train branch networks. Each branch module, which is a three-layer hierarchical network, has a restricted number of output neurons and inter-connections so that it is easy to train. Therefore CombNET-II does not cause the local minimum state since the complexities of the problems to be solved for each branch module are restricted by the stem network. CombNET-II correctly classified 99.0% of previously unseen handwritten alpha-numeric characters.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114433193","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: 12
Nonlinear estimation of torque in switched reluctance motors using grid locking and preferential training techniques on self-organizing neural networks 基于网格锁定和自组织神经网络优先训练技术的开关磁阻电机转矩非线性估计
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.226887
J.J. Garside, R. Brown, T.L. Ruchti, X. Feng
{"title":"Nonlinear estimation of torque in switched reluctance motors using grid locking and preferential training techniques on self-organizing neural networks","authors":"J.J. Garside, R. Brown, T.L. Ruchti, X. Feng","doi":"10.1109/IJCNN.1992.226887","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.226887","url":null,"abstract":"The torque of a switched reluctance motor (SRM) can be estimated using a topology-preserving self-organizing neural network map. Since self-organizing maps tend to contract at region boundaries, a procedure for locking neuron weights at specific locations in a region is presented. A strategy for preferentially training neuron weights on the region boundaries is introduced. As an example of these training techniques, a one-dimensional neural network will approximate a nonlinear function. In general an n-dimension mapping can be used to approximate an m-dimensional system for n<or=m. As a practical implementation of this technique, the modeling of the theoretical torque of a SRM as a function of position and current is presented. A two-dimensional neural network estimates a three-dimensional highly nonlinear surface.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122058596","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: 6
A dynamic approach to improve sparsely encoded associative memory capability 一种提高稀疏编码联想记忆能力的动态方法
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.287169
Y.-P. Huang, D. Gustafson
{"title":"A dynamic approach to improve sparsely encoded associative memory capability","authors":"Y.-P. Huang, D. Gustafson","doi":"10.1109/IJCNN.1992.287169","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.287169","url":null,"abstract":"A method for improving sparsely encoded associative memory storage capacity based on dynamic thresholding is presented. Under the dynamic thresholding scheme, the sparsely encoding method is shown to have greater storage capacity than the ordinary associative memory. The results are also considered from the storage sensitivity point of view. Simulation results are consistent with the quantitative analysis. It is found that system capacity is strongly dependent on the selected threshold. Selection of threshold is based on each neuron working close to its threshold assumption. This makes it possible to find a more reasonable storage capacity by using signal part and mean noise only.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116812436","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
A genetic approach to the truck backer upper problem and the inter-twined spiral problem 基于遗传算法的卡车后轮上部问题和缠绕螺旋问题
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.227324
J. Koza
{"title":"A genetic approach to the truck backer upper problem and the inter-twined spiral problem","authors":"J. Koza","doi":"10.1109/IJCNN.1992.227324","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.227324","url":null,"abstract":"The author describes a biologically motivated paradigm, genetic programming, which can solve a variety of problems. When genetic programming solves a problem, it produces a computer program that takes the state variables of the system as input and produces the actions required to solve the problem as output. Genetic programming is explained and applied to two well-known benchmark problems from the field of neural networks. The truck backer upper problem is a multidimensional control problem and the inter-twined spirals problem is a challenging classification problem.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116846037","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: 82
A fuzzy neural networks technique with fast backpropagation learning 一种快速反向传播学习的模糊神经网络技术
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.287133
H. Y. Xu, G.Z. Wang, C.B. Baird
{"title":"A fuzzy neural networks technique with fast backpropagation learning","authors":"H. Y. Xu, G.Z. Wang, C.B. Baird","doi":"10.1109/IJCNN.1992.287133","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.287133","url":null,"abstract":"A fuzzy neural network (FNN) technique is presented based on fuzzy systems and neural network technologies. Utilizing human knowledge and expertise, the FNN technique is applied to accelerate the learning process of a novel backpropagation algorithm in which both self-adjusting activation and learning rate functions are designated. The learning speed and quality of the fuzzy neural networks are proved to be superior to those of standard backpropagation and other methods using changeable learning rates or activation functions. The proposed networks are currently developed and implemented in a C language environment. Experimental and analytical results demonstrate that the FNN technique is a novel and potentially powerful approach to intelligent neural networks.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129844998","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
A software reconfigurable multi-networks simulator using a custom associative chip 使用自定义关联芯片的软件可重构多网络模拟器
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI: 10.1109/IJCNN.1992.226991
J. Gascuel, E. Delaunay, L. Montoliu, B. Moobed, M. Weinfeld
{"title":"A software reconfigurable multi-networks simulator using a custom associative chip","authors":"J. Gascuel, E. Delaunay, L. Montoliu, B. Moobed, M. Weinfeld","doi":"10.1109/IJCNN.1992.226991","DOIUrl":"https://doi.org/10.1109/IJCNN.1992.226991","url":null,"abstract":"A special-purpose simulator is described. It has been designed to try various interconnection schemes between several similar associative chips, in order to assess hierarchical assemblies of neural networks. These chips are digital feedback networks with 64 fully interconnected binary neurons, capable of on-chip learning and automatic detection of spurious attractors. This simulator is based on the MCP development board. Each such board can house four associative chips. The simulator is designed to transparently address chips not only inside the machine in which it resides, but also chips in other machines. All the virtual interconnections between chips are made at the neuron level, which means that the individual components of binary vectors processed by each chip can be routed to the input or from the output of any other chip. Simulator scheduling allows sequentiality in information processing.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880562","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
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