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2021 International Conference on Neuromorphic Computing (ICNC)最新文献

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On Synchronization of Coupled Fractional-Order Neural Networks in Finite-Time Sense 有限时间意义上耦合分数阶神经网络的同步问题
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608620
Minglin Xu, Yunliu Li, Peng Liu, Peizhao Yu, Sanyi Li, Junwei Sun
{"title":"On Synchronization of Coupled Fractional-Order Neural Networks in Finite-Time Sense","authors":"Minglin Xu, Yunliu Li, Peng Liu, Peizhao Yu, Sanyi Li, Junwei Sun","doi":"10.1109/icnc52316.2021.9608620","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608620","url":null,"abstract":"This paper focuses on the synchronization of coupled fractional-order neural networks with time-varying delays in finite-time sense. By introducing an effective controller, the synchronization of coupled fractional-order neural networks is realized in finite time, and a settling time can be estimated. Moreover, sufficient criteria are deduced to ensure the realization of synchronization in finite-time sense. The results of this paper are general and are also applicable for the synchronization of integer-order neural networks. Finally, a numerical example is provided to verify the correctness and validity of the theoretical results.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121062991","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
Digital Hardware Implementation of Optimized Spiking Neurons 优化尖峰神经元的数字硬件实现
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608391
Sven Nitzsche, Brian Pachideh, Nicolas Luhn, Jrgen Becker
{"title":"Digital Hardware Implementation of Optimized Spiking Neurons","authors":"Sven Nitzsche, Brian Pachideh, Nicolas Luhn, Jrgen Becker","doi":"10.1109/icnc52316.2021.9608391","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608391","url":null,"abstract":"Neuromorphic computing and spiking neural networks have seen growing attention lately, especially in combination with event-based sensors and in robotics. To deploy such spiking neural networks, suitable hardware platforms are required. Neuromorphic hardware can be built based on various neuron models. However, only few publications so far investigate which neuron models are best suited for hardware implementation and many platforms use simple models like leaky integrate-and-fire. This work investigates how various neuron models can be implemented in hardware, applying different optimization and approximation techniques. The optimized neurons are prototyped on a field-programmable gate array and compared based on hardware resource requirements, power consumption and accuracy versus non-optimized models. Promising candidates for hardware implementation are pointed out and different metrics are evaluated, providing a guideline to trade-off the hardware implementation and performance for different neuron types.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332776","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
Real-Time Power System State Estimation 实时电力系统状态估计
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608876
Lianjiao Wang, Huiwei Wang
{"title":"Real-Time Power System State Estimation","authors":"Lianjiao Wang, Huiwei Wang","doi":"10.1109/icnc52316.2021.9608876","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608876","url":null,"abstract":"Power system state estimation (PSSE) is one of the most important functions in power system operation and control. A key challenge for precise PSSE is the inherent nonlinearity of measurements with respect to system states. In this paper, the convex relaxation method is used for removing the nonlinearity in the PSSE problem. Moreover, the alternating direction multiplier method (ADMM) is utilized for addressing the real-time PSSE problem. In addition, the vectorization operator are developed for solving the quadratic matrix equation and accelerating the ADMM. Finally, the feasibility and effectiveness of our method are verified via a benchmark case on IEEE 14-bus system.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125066433","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
Multistability of Memristor-Based Impulsive Neural Networks With Time-Varying Delays 时变时滞忆阻器脉冲神经网络的多稳定性
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608029
Yuanchu Shen, Song Zhu
{"title":"Multistability of Memristor-Based Impulsive Neural Networks With Time-Varying Delays","authors":"Yuanchu Shen, Song Zhu","doi":"10.1109/icnc52316.2021.9608029","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608029","url":null,"abstract":"This paper investigates multistability of memristor-based impulsive neural networks with time-varying delays. By using the state space partition method and Lyapunov functions, a sufficient condition is obtained to ensure there are $(2K+1)^{n}$ equilibrium points coexist in a $n$ neurons networks, where $K$ is a parameter concerned with the activation function, and $(K+1)^{n}$ of them are locally stable. At the end of this paper, the conclusions are further summarized.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114487978","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
Carbon-Aware Economic Dispatch: A Singular Perturbation Approach 碳意识经济调度:奇异扰动方法
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608719
Wen-Ting Lin, Chaojie Li
{"title":"Carbon-Aware Economic Dispatch: A Singular Perturbation Approach","authors":"Wen-Ting Lin, Chaojie Li","doi":"10.1109/icnc52316.2021.9608719","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608719","url":null,"abstract":"With the worsen of environmental problems, the carbon emission of the energy system has quickly become the spotlight. However, Most of the existing works focus on the economic dispatch without carbon emission constraints. In this paper, we focus on the economic dispatch with carbon emission constraints. First, a novel mathematical model which characterizes the economic dispatch with carbon emission constraints is given. Then it is followed by the distributed carbon-aware dispatch strategy design based on the singular perturbation system.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241109","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
Finite-time synchronization of memristive neural networks via quantized intermittent control 基于量化间歇控制的记忆神经网络有限时间同步
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608042
Wenqiang Yang, Junjian Huang, Zhizhang Li, Changfu Chen, Zili Cao, Xiao Li
{"title":"Finite-time synchronization of memristive neural networks via quantized intermittent control","authors":"Wenqiang Yang, Junjian Huang, Zhizhang Li, Changfu Chen, Zili Cao, Xiao Li","doi":"10.1109/icnc52316.2021.9608042","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608042","url":null,"abstract":"This paper focuses on finite-time synchronization (FTS) based on quantized intermittent control of memristive neural networks (MNN). The synchronization of the system constructed under the concept of Filippov solution is achieved by the design of a new quantized intermittent controller (QIC). The corresponding conditions for achieving system synchronization are established based on FTS theory and intermittent strategies. Finally, a numerical simulation is given to verify the correctness of the theory.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387683","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
A class of offset boostable 3-D memristive system 一类偏置可升压三维记忆系统
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608241
Chunbiao Li, Ran Wang, Xu Ma, Yicheng Jiang
{"title":"A class of offset boostable 3-D memristive system","authors":"Chunbiao Li, Ran Wang, Xu Ma, Yicheng Jiang","doi":"10.1109/icnc52316.2021.9608241","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608241","url":null,"abstract":"Offset boosting of a chaotic signal is the key issue for chaos production. Memristor is a new nonlinear component that promotes chaotic oscillator design for its integrated memory dynamics. By memristor introducing variable boostable dynamical flows can be upgraded to be offset boostable memristive chaotic systems. Following this routine, a memristor can be planted into a dynamical flow by direct introducing, cross term matching, feedback augment, and internal variable transferring. Easy offset boosting is discussed and demonstrated by linearly shifted average values under a unified set of Lyapunov exponents. However, if the boostable system variable is regarded as the internal variable of a memristor, offset boosting with this dimension becomes not necessary for the corresponding system but leaves a shortcut for offset boosting if the system is upgraded with dimension.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218133","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
Finite-time Synchronization of Fractional-order Delayed Memristive Neural Networks by Discontinuous Control Scheme 基于不连续控制的分数阶延迟记忆神经网络有限时间同步
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9607939
Guan-Sheng Wang, Rui Jiao, Tingting Su, Zhixia Ding, Sai Li, Liheng Wang
{"title":"Finite-time Synchronization of Fractional-order Delayed Memristive Neural Networks by Discontinuous Control Scheme","authors":"Guan-Sheng Wang, Rui Jiao, Tingting Su, Zhixia Ding, Sai Li, Liheng Wang","doi":"10.1109/icnc52316.2021.9607939","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9607939","url":null,"abstract":"In this paper, the problem of the finite-time synchronization is investigated about a class of fractional-order delayed memristive neural networks (FDMNNs). Under the framework of Filippov solution, a discontinuous feedback controller is designed to achieve finite-time synchronization of such systems. In addition, the upper limit of synchronization time is estimated by fractional-order differential inequality. Finally, a numerical example is given to show the correctness of the conclusion.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130731589","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
Saturation region attractivity analysis for memristive neural networks under the strong external input 强外部输入下记忆神经网络的饱和区吸引性分析
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608894
Gang Bao, Xue Zhou, Wenbin Hu
{"title":"Saturation region attractivity analysis for memristive neural networks under the strong external input","authors":"Gang Bao, Xue Zhou, Wenbin Hu","doi":"10.1109/icnc52316.2021.9608894","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608894","url":null,"abstract":"Memristive neural networks are recurrent neural networks with memristors as connection weights. Such n dimensional neural networks can be seen as a family of 2n2sub networks. By math analysis method, we discuss saturation region attractivity of such neural networks. Then we present one sufficient condition for networks saturation region attractivity with the strong external input. At last, one examples is given for verify our results.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581135","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
A Study on Calculating and Counting the Low-cost Paths in Bus Transportation Networks 公交网络中低成本路径的计算与计数研究
2021 International Conference on Neuromorphic Computing (ICNC) Pub Date : 2021-10-15 DOI: 10.1109/icnc52316.2021.9608125
Guang Chen, Hong-xiang Hu
{"title":"A Study on Calculating and Counting the Low-cost Paths in Bus Transportation Networks","authors":"Guang Chen, Hong-xiang Hu","doi":"10.1109/icnc52316.2021.9608125","DOIUrl":"https://doi.org/10.1109/icnc52316.2021.9608125","url":null,"abstract":"Multiple low-cost paths may exist between two stops in a bus transportation network (BTN). In most previous studies, only the lowest-cost paths were considered. By fully taking account of all these paths, more accurate and effective results of BTN researches may be established. In this paper, we present a preliminary study on calculating and counting the low-cost paths in a BTN. Specifically, we present an algorithm of calculating the low-cost paths between two arbitrary stops in BTNs, which takes account of both the bus riding edges and short-distance walking edges. Besides, a case study based on the BTN of Hangzhou in China of analyzing the number of the low-cost paths with transfer times less than 3 is given. Results show that the number of the low-cost paths between two stops increases exponentially with passengers' tolerance degree to the travel cost, and the number is sensitive to the factors of transfer-times and walking distance.","PeriodicalId":412866,"journal":{"name":"2021 International Conference on Neuromorphic Computing (ICNC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227440","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
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