The 2013 International Joint Conference on Neural Networks (IJCNN)最新文献_第2页

Unsupervised collaborative boosting of clustering: An unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering 无监督协同提升聚类:多视图聚类、多共识聚类和替代聚类的统一框架

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706911

Jacques-Henri Sublemontier

引用次数: 5

Recurrent neural networks with fixed time convergence for linear and quadratic programming 线性和二次规划的固定时间收敛递归神经网络

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706835

J. Sánchez‐Torres, E. Sánchez, A. Loukianov

引用次数: 11

Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator SpiNNaker神经模拟模拟器传递函数的基于spike的学习

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706962

Sergio Davies, T. Stewart, C. Eliasmith, S. Furber

引用次数: 5

Human behaviour recognition based on trajectory analysis using neural networks 基于神经网络轨迹分析的人类行为识别

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706724

J. A. López, M. Saval-Calvo, Andrés Fuster Guilló, J. G. Rodríguez

引用次数: 20

Correctness and performance of the SpiNNaker architecture SpiNNaker架构的正确性和性能

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706988

T. Sharp, S. Furber

引用次数: 26

Solutions to finite horizon cost problems using actor-critic reinforcement learning 有限视界成本问题的actor-critic强化学习解决方案

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706755

I. Grondman, Hao Xu, S. Jagannathan, Robert Babuška

{"title":"Solutions to finite horizon cost problems using actor-critic reinforcement learning","authors":"I. Grondman, Hao Xu, S. Jagannathan, Robert Babuška","doi":"10.1109/IJCNN.2013.6706755","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706755","url":null,"abstract":"Actor-critic reinforcement learning algorithms have shown to be a successful tool in learning the optimal control for a range of (repetitive) tasks on systems with (partially) unknown dynamics, which may or may not be nonlinear. Most of the reinforcement learning literature published up to this point only deals with modeling the task at hand as a Markov decision process with an infinite horizon cost function. In practice, however, it is sometimes desired to have a solution for the case where the cost function is defined over a finite horizon, which means that the optimal control problem will be time-varying and thus harder to solve. This paper adapts two previously introduced actor-critic algorithms from the infinite horizon setting to the finite horizon setting and applies them to learning a task on a nonlinear system, without needing any assumptions or knowledge about the system dynamics, using radial basis function networks. Simulations on a typical nonlinear motion control problem are carried out, showing that actor-critic algorithms are capable of solving the difficult problem of time-varying optimal control. Moreover, the benefit of using a model learning technique is shown.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115119730","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

Incremental anomaly identification by adapted SVM method 基于改进支持向量机的增量异常识别

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707031

M. Suvorov, S. Ivliev, Garegin Markarian, Denis Kolev, Dmitry Zvikhachevskiy, P. Angelov

{"title":"Incremental anomaly identification by adapted SVM method","authors":"M. Suvorov, S. Ivliev, Garegin Markarian, Denis Kolev, Dmitry Zvikhachevskiy, P. Angelov","doi":"10.1109/IJCNN.2013.6707031","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6707031","url":null,"abstract":"In our work we used the capability of one-class support vector machine (SVM) method to develop a novel one-class classification approach. Algorithm is designed and tested within the project SVETLANA aimed for fault detection in complex technological systems, such as aircraft. The main objective of this project was to create an algorithm responsible for collecting and analyzing the data since the launch of an aircraft engine. Data can be transferred from a variety of sensors that are responsible for the speed, oxygen level etc. In order to apply real time (in flight) application a recursive learning algorithm is proposed. The proposed method analyzes both “positive”/”normal” and “negative”/ “abnormal” examples The overall model structure is the same as an outlier-detection approach. The most important benefits of the new algorithm based on our algorithm are verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the USA, Western European as well as Russia. The test results are presented in the final part of the article.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115315473","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

Authenticated key exchange protocol using neural cryptography with secret boundaries 使用具有秘密边界的神经密码学的认证密钥交换协议

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707125

A. M. Allam, H. Abbas, M. El-Kharashi

{"title":"Authenticated key exchange protocol using neural cryptography with secret boundaries","authors":"A. M. Allam, H. Abbas, M. El-Kharashi","doi":"10.1109/IJCNN.2013.6707125","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6707125","url":null,"abstract":"Key exchange is one of the major concerns in cryp-tology. Neural cryptography is a recent non-classical paradigm which achieves key exchange by mutual learning between two neural networks that receive the same input patterns and update their weights using specific rules. Each weight component of the network can be seen as a random walker in the weight space. The two walkers move in the weight space and reflect at two boundaries (left and right) which represent the network synaptic depth. The reflecting boundaries cause the distance between the two walkers decreases if one of them hits the boundary when a common direction is chosen at each step. Therefore, the mutual learning algorithm relies on this defined boundary condition to achieve synchronization between the two parties. In this paper, we aim to increase the security of the neural cryptography by authenticating the communication using preshared secrets. The mutual learning algorithm is modified so that the reflecting boundaries become hidden and only accessible by the two partners. New update rules are developed to exploit the secret information without adding any limitation to the initial configuration for the two parties. This is done by converting the two boundaries located at a straight line path to a one secret boundary located randomly at a circular path. Therefore, the mutual learning is impeded except this secret information is known. The proposed algorithm is called Neural Cryptography with Secret Boundaries (NCSB) and it is proved with information theory that the secret boundaries can not be revealed from the public information broadcast through the public channel.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115666413","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: 25

The added value of gating in evolved neurocontrollers 门控在进化的神经控制器中的附加价值

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706895

Timur Chabuk, J. Reggia

{"title":"The added value of gating in evolved neurocontrollers","authors":"Timur Chabuk, J. Reggia","doi":"10.1109/IJCNN.2013.6706895","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706895","url":null,"abstract":"While the concept of gating has been explored in past studies of neural networks, and neural network controllers have been successfully designed through evolutionary computation methods, very little past work has focused on empirically determining the value of adding gating to evolved neural network architectures. In this study, we do precisely that, by examining a neural architecture and genetic representation that explicitly permits the use of gating connections in a neurocontroller, and comparing the evolved controller performance to similar evolved controllers where gating connections are not explicitly included. The performance of these different approaches is evaluated in evolving a neurocontroller for an autonomous agent navigating through a simulated predator-prey environment. We find that the neural architecture that explicitly allows gating clearly outperforms three other architectures without gating, suggesting that there is a clear benefit to having gating connections directed by a command module. Further analysis of the best evolved agent reveals that its controller executes by producing command signals that encode high-level goals, which then modify low-level behaviors to achieve those goals, supporting the hypothesis that allowing gated connections in neural networks substantially improves the neurocontrollers that can be evolved.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396067","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

Contradiction resolution with explicit and limited evaluation and its application to SOM 明确和有限评价的矛盾解决及其在SOM中的应用

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706999

R. Kamimura

引用次数: 0