2015 International Joint Conference on Neural Networks (IJCNN)最新文献_第7页

Tactile sequence classification using joint kernel sparse coding 基于联合核稀疏编码的触觉序列分类

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280512

Jingwei Yang, Huaping Liu, F. Sun, Meng Gao

引用次数: 16

Modelling Absence Epilepsy seizure data in the NeuCube evolving spiking neural network architecture 模拟缺席癫痫发作数据在neurocube不断发展的尖峰神经网络结构

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280764

E. Capecci, Josafath Israel Espinosa Ramos, N. Mammone, N. Kasabov, J. Duun-Henriksen, T. Kjaer, M. Campolo, F. L. Foresta, F. Morabito

引用次数: 3

Classification of migraine stages based on resting-state EEG power 基于静息状态脑电图功率的偏头痛分期

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280582

Zehong Cao, L. Ko, K. Lai, Song-Bo Huang, Shuu-Jiun Wang, Chin-Teng Lin

{"title":"Classification of migraine stages based on resting-state EEG power","authors":"Zehong Cao, L. Ko, K. Lai, Song-Bo Huang, Shuu-Jiun Wang, Chin-Teng Lin","doi":"10.1109/IJCNN.2015.7280582","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280582","url":null,"abstract":"Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.σ","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"38 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74703307","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

Discriminative dimensionality reduction for regression problems using the Fisher metric 使用Fisher度量的回归问题判别降维

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280736

Alexander Schulz, B. Hammer

引用次数: 3

One-shot Training of Polynomial Cellular Neural Networks and applications in image processing 多项式细胞神经网络的一次训练及其在图像处理中的应用

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280369

A. Arista-Jalife, E. Gómez-Ramírez

引用次数: 0

Hopfield networks: from optimization to adaptive control Hopfield网络:从优化到自适应控制

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280368

M. Atencia, G. Joya

{"title":"Hopfield networks: from optimization to adaptive control","authors":"M. Atencia, G. Joya","doi":"10.1109/IJCNN.2015.7280368","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280368","url":null,"abstract":"This paper proposes an adaptive control algorithm, which is designed by adding a parametric identification method to a non-linear controller. The identification module is built upon the Hopfield neural network, resulting in an unconventional network with time-varying weights and biases. The convergence of the estimations of the parameters of a dynamical system was proved in previous work, as long as the system inputs can be freely manipulated to provide persistent excitation. Henceforth the behaviour of the closed-loop system, when the inputs result from the controller equations, is here analyzed in order to assess both the tracking performance of the full adaptive controller and the identification ability of the neural estimator. The algorithm is applied to an idealized robotic system with two joints, whose positions and velocities are required to follow, as closely as possible, a prescribed reference trajectory. The simulation results show a satisfactory control performance, since the demanded trajectory is almost accurately followed. The estimated values also converge to the correct parameters, as long as the controller provides sufficiently rich signals to the system. The results are similar to a conventional least-squares adaptive controller, with a much lower computational cost.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"70 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74093713","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

Artificial motivations based on drive-reduction theory in self-referential model-building control systems 基于驱动缩减理论的自参照建模控制系统人工动机

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280623

Moritz Schneider, J. Adamy

{"title":"Artificial motivations based on drive-reduction theory in self-referential model-building control systems","authors":"Moritz Schneider, J. Adamy","doi":"10.1109/IJCNN.2015.7280623","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280623","url":null,"abstract":"Motivation and emotion are inseparable component factors of value systems in living beings, which enable them to act purposefully in a partially unknown and sometimes unforgiving environment. Value systems that drive innate reinforcement learning mechanisms have been identified as key factors in self-directed control and autonomous development towards higher intelligence and seem crucial in the development of a concept of “self” in sentient beings [1]. This contribution is concerned with the relationship between artificial learning control systems and innate value systems. In particular, we adapt the state-of-the-art model of motivational processes based on reduction of generalized drives towards higher flexibility, expressivity and representation capability. A framework for modelling self-adaptive value systems, which develop autonomously starting from an inherited (or designed) innate representation, within a learning control system architecture is formulated. We discuss the relationship of anticipated effects in this control architecture with psychological theory on motivations and contrast our framework with related approaches.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"27 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84809301","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

A probability-dynamic Particle Swarm Optimization for object tracking 基于概率动态粒子群算法的目标跟踪

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280515

Feng Sha, C. Bae, Guang Liu, XiMeng Zhao, Yuk Ying Chung, W. Yeh, Xiangjian He

{"title":"A probability-dynamic Particle Swarm Optimization for object tracking","authors":"Feng Sha, C. Bae, Guang Liu, XiMeng Zhao, Yuk Ying Chung, W. Yeh, Xiangjian He","doi":"10.1109/IJCNN.2015.7280515","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280515","url":null,"abstract":"Particle Swarm Optimization has been used in many research and application domain popularly since its development and improvement. Due to its fast and accurate solution searching, PSO has become one of the high potential tools to provide better outcomes to solve many practical problems. In image processing and object tracking applications, PSO also indicates to have good performance in both linear and non-linear object moving pattern, many scientists conduct development and research to implement not only basic PSO but also improved methods in enhancing the efficiency of the algorithm to achieve precise object tracking orbit. This paper is aim to propose a new improved PSO by comparing the inertia weight and constriction factor of PSO. It provides faster and more accurate object tracking process since the proposed algorithm can inherit some useful information from the previous solution to perform the dynamic particle movement when other better solution exists. The testing experiments have been done for different types of video, results showed that the proposed algorithm can have better quality of tracking performance and faster object retrieval speed. The proposed approach has been developed in C++ environment and tested against videos and objects with multiple moving patterns to demonstrate the benefits with precise object similarity.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"61 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84896783","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: 9

Probabilistic temporal bilinear model for temporal dynamic recommender systems 时间动态推荐系统的概率时间双线性模型

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280617

Cheng Luo, Xiongcai Cai, N. Chowdhury

{"title":"Probabilistic temporal bilinear model for temporal dynamic recommender systems","authors":"Cheng Luo, Xiongcai Cai, N. Chowdhury","doi":"10.1109/IJCNN.2015.7280617","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280617","url":null,"abstract":"User preferences for products are constantly drifting over time as product perception and popularity are changing when new fashions or products emerge. Therefore, the ability to model the tendency of both user preferences and product attractiveness is vital to the design of recommender systems (RSs). However, conventional methods in RSs are incapable of modeling such a tendency accordingly, leading to unsatisfactory recommendation performance in many real-world deployments. In this paper, we develop a novel probabilistic temporal bilinear model for RSs, exploiting both temporal properties and dynamic information in user preferences and item attractiveness derived from the users' feedback over items, to simultaneously track latent factors that represent user preferences and item attractiveness. A learning and inference algorithm combining a sequential Monte Carlo method and the EM algorithm for this model is also developed to tackle the top-k recommendation problem over time. The proposed model is evaluated on three benchmark datasets. The experimental results demonstrate that our proposed model significantly outperforms a variety of existing methods for top-k recommendation.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"35 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85007625","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

An efficient SpiNNaker implementation of the Neural Engineering Framework 神经工程框架的高效SpiNNaker实现

2015 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2015-07-12 DOI: 10.1109/IJCNN.2015.7280390

Andrew Mundy, James C. Knight, T. Stewart, S. Furber

{"title":"An efficient SpiNNaker implementation of the Neural Engineering Framework","authors":"Andrew Mundy, James C. Knight, T. Stewart, S. Furber","doi":"10.1109/IJCNN.2015.7280390","DOIUrl":"https://doi.org/10.1109/IJCNN.2015.7280390","url":null,"abstract":"By building and simulating neural systems we hope to understand how the brain may work and use this knowledge to build neural and cognitive systems to tackle engineering problems. The Neural Engineering Framework (NEF) is a hypothesis about how such systems may be constructed and has recently been used to build the world's first functional brain model, Spaun. However, while the NEF simplifies the design of neural networks, simulating them using standard computer hardware is still computationally expensive - often running far slower than biological real-time and scaling very poorly: problems the SpiNNaker neuromorphic simulator was designed to solve. In this paper we (1) argue that employing the same model of computation used for simulating general purpose spiking neural networks on SpiNNaker for NEF models results in suboptimal use of the architecture, and (2) provide and evaluate an alternative simulation scheme which overcomes the memory and compute challenges posed by the NEF. This proposed method uses factored weight matrices to reduce memory usage by around 90% and, in some cases, simulate 2000 neurons on a processing core - double the SpiNNaker architectural target.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"48 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85078014","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: 53