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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 2988 条记录，以下是141-150 订阅

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Dynamic Modelling of Supercapacitor Using Artificial neural network Technique 8

Dynamic Modelling of Supercapacitor Using Artificial Neural ...

引用

8th International conference And Exposition On Electrical And Power Engineering (EPE)

作者： Danila, Elena Livint, Gheorghe Lucache, Dorin Dumitru Dept. of Energy Utilisation Tech. Univ. Gheorghe Asachi of Iasi Iasi Romania

ISBN: (纸本)9781479958498

Because of the complex dynamic behavior of supercapacitor, its modeling must be based on parallel, distributed structures (each component has to represent a model of activity, distributed on many processing units), with learning capacity. For this purpose, the paper proposes a new feed forward artificial neural network structure with two hidden layers and with backpropagation training. The neural network provides, after activation, training, testing and reinitializing, output values with a total correlation of 0, 9426 compared with target values.

关键词： supercapacitor modelling artificial neural network correlation

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FIST: A Framework to Interleave Spiking neural networks on CGRAs 23

FIST: A Framework to Interleave Spiking neural networks on C...

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23rd Euromicro International conference on Parallel, distributed, and network-Based processing (PDP)

作者： Tuan Ngyen Jafri, Syed M. A. H. Daneshtalab, Masoud Hemani, Ahmed Dytckov, Sergei Plosila, Juha Tenhunen, Hannu Turku Ctr Comp Sci Turku Finland Univ Turku SF-20500 Turku Finland Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781479984909

Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern embedded applications. In many application domains (e.g. robotics and cognitive embedded systems), the CGRAs are required to simultaneously host processing (e.g. Audio/video acquisition) and estimation (e.g. audio/video/image recognition) tasks. Recent works have revealed that the efficiency and scalability of the estimation algorithms can be significantly improved by using neural networks. However, existing CGRAs commonly employ homogeneous processing resources for both the tasks. To realize the best of both the worlds (conventional processing and neural networks), we present FIST. FIST allows the processing elements and the network to dynamically morph into either conventional CGRA or a neural network, depending on the hosted application. We have chosen the DRRA as a vehicle to study the feasibility and overheads of our approach. Synthesis results reveal that the proposed enhancements incur negligible overheads (4.4% area and 9.1% power) compared to the original DRRA cell.

关键词： neural networks edge detection neuro morphic systems re configurable architectures neural network Edge detection Coarse-grained Reconfigurable Architecture robotics Scalability HIGH-PERFORMANCE spiking

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distributed Approximating Functional Wavelet based neural network in ECG signal processing

Distributed Approximating Functional Wavelet based neural ne...

引用

IEEE International conference on Automation, Quality and Testing, Robotics, AQTR

作者： Z. German-Sallo L. David Petru Maior University of Tg Mureş Romania

For ECG signal processing, information extraction from a noisy background is the fundamental objective. Filtering (noise suppression, baseline wander elimination) is a very important step in efficient ECG signal features extracting to enhance the performance of automatic detection and classification of different cardiac diseases. In this paper we used distributed approximating functional (DAF) wavelets to develop algorithms for signal approximation and filtering. These algorithms use moving average artificial neural network with wavelet type Hermite activating function. They are evaluated in MATLAB with signals from the MIT-BIH arrhythmia database and comparisons are made with the classical (radial basis function and sigmoid type activating function) artificial neuronal networks. New functions were created and integrated into MATLAB environment. The outcomes indicate a good performance tradeoff between accuracy and response time, making this type of algorithms desirable also for real-time implementation

关键词： neural networks Electrocardiography Signal processing Signal processing algorithms Data mining Filtering MATLAB Background noise Feature extraction Cardiac disease

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STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS WITH GRAPH neural networkS 49

STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Das, Sourajit NaderiAlizadeh, Navid Ribeiro, Alejandro Univ Penn Philadelphia PA 19104 USA Duke Univ Durham NC USA

ISBN: (纸本)9798350344868;9798350344851

We consider the problem of routing network packets in a large-scale communication system where the nodes have access to only local information. We formulate this problem as a constrained learning problem, which can be solved using a distributed optimization algorithm. We approach this distributed optimization using a novel state-augmentation (SA) strategy to maximize the aggregate information packets at different source nodes, leveraging dual variables corresponding to flow constraint violations. The construction is based on graph neural networks (GNNs) that employ graph convolutions over the underlying communication network topology. We devise an unsupervised learning algorithm to transform the output of the GNN architecture into optimal routing decisions. The proposed method takes advantage of only the local information available at each node and efficiently routes the desired packets to the destination. We provide numerical results demonstrating the superiority of the proposed method over baseline routing algorithms.

关键词： Routing distributed optimization graph neural networks unsupervised learning Lagrangian duality

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Histogram-based image classification using fuzzy ARTMAP neural network

Histogram-based image classification using fuzzy ARTMAP neur...

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conference on neural network and distributed processing

作者： Jiang, CH Chen, Z Beijing Univ Aeronaut & Astronaut Beijing 100083 Peoples R China

ISBN: (纸本)0819442836

This paper presents a novel fast images classification method which based on image histogram features and using fuzzy ARTMAP neural network. Compared with the previous method, the edge, brightness, contrast and SNR feature of images are taken into account in this method, and it has so many advantages such as self-adaptive clustering, fast convergence, good real-time ability, high classification accuracy and high universality etc. It can be adopted in SMGS (scene matching guidance system) to auto-select real-time images and so as to improve the level of intelligence, reliability and real time ability of SMGS.

关键词： histogram feature fuzzy ARTMAP neural network image classification SMGS

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Combining Parallel Genetic Algorithms and Machine Learning to improve the research of optimal vaccination protocols 26

Combining Parallel Genetic Algorithms and Machine Learning t...

引用

26th Euromicro International conference on Parallel, distributed, and network-Based processing (PDP)

作者： Pennisi, Marzio Russo, Giulia Pappalardo, Francesco Univ Catania Dept Math & Comp Sci Catania Italy Univ Catania Dept Biomed Sci Catania Italy Univ Catania Dept Drug Sci Catania Italy

ISBN: (纸本)9781538649756

The developing of novel prophylactic and therapeutic vaccine candidates in the field of cancer immunology brought to very promising results against tumors, entitling full protection with reduced amount of the typical side effects of the actual conventional treatments. However, such treatments required a constant, life-long, administration procedure to keep protection. As both the period of protection and the relative number of administrations grow, the problem of finding the best administration protocol, in time and dosage, becomes more and more complex. Such a problem cannot be usually solved in in vivo experiments, as the costs in terms of time, money, and people would be prohibitive. We propose a hybrid approach that integrates machine learning and parallel genetic algorithms to enhance the research in silico of optimal administration protocols for a cancer vaccine. A neural network is used to improve both crossover and mutation operators. Preliminary results suggest that the use of such could bring to better administration protocols using a similar computational effort.

关键词： Genetic algorithms machine learning neural networks agent based models optimal vaccine protocol cancer

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distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems 36

Distributed Inverse Constrained Reinforcement Learning for M...

引用

36th conference on neural Information processing Systems (NeurIPS)

作者： Liu, Shicheng Zhu, Minghui Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

ISBN: (纸本)9781713871088

This paper considers the problem of recovering the policies of multiple interacting experts by estimating their reward functions and constraints where the demonstration data of the experts is distributed to a group of learners. We formulate this problem as a distributed bi-level optimization problem and propose a novel bi-level "distributed inverse constrained reinforcement learning" (D-ICRL) algorithm that allows the learners to collaboratively estimate the constraints in the outer loop and learn the corresponding policies and reward functions in the inner loop from the distributed demonstrations through intermittent communications. We formally guarantee that the distributed learners asymptotically achieve consensus which belongs to the set of stationary points of the bi-level optimization problem. Simulations are done to validate the proposed algorithm.

关键词： Multi agent systems

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Application of Higher Order neural network Dynamics to distributed Radio Resource Usage Optimization of Cognitive Wireless networks

Application of Higher Order Neural Network Dynamics to Distr...

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15th International conference on Neuro-Information processing

作者： Hasegawa, Mikio Takeda, Taichi Kuroda, Taro Tran, Ha Nguyen Miyamoto, Goh Murata, Yoshitoshi Harada, Hiroshi Kato, Shuzo Tokyo Univ Sci Tokyo 1020073 Japan NICT Yokosuka Kanagawa 2390847 Japan Iwate Prefectural Univ Takizawa Japan

ISBN: (纸本)9783642024894

We propose a distributed radio access network selection method for heterogeneous wireless network environment, in which mobile terminals call adaptively and seamlessly handover among different wireless access technologies. Our algorithm optimizes fairness of radio resource usage without centralized computing on the network side. As a decentralized optimization scheme, we introduce the dynamics of the mutually connected neural network dynamics, whose energy function autonomously minimizes by distributed update of each neuron. Since the objective function of the fairness becomes a fourth-order function of the neurons' states which cannot be optimized by the conventional Hopfield neural network, we apply a neural network model extended to higher-order mutual connections and energy functions. By numerical simulation, we confirm that the proposed algorithm call optimize fairness of the throughput by distributed and autonomous computation.

关键词： Heterogeneous networks

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A Low Complexity Decentralized neural Net with Centralized Equivalence using Layer-wise Learning

A Low Complexity Decentralized Neural Net with Centralized E...

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International Joint conference on neural networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Liang, Xinyue Javid, Alireza M. Skoglund, Mikael Chatterjee, Saikat KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden

ISBN: (纸本)9781728169262

We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication network between the workers is synchronized and can be modeled as a doubly-stochastic mixing matrix without having any master node. In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. Using altemating-direction-method-of-multipliers (ADMM) along with a layer-wise convex optimization approach, we propose a decentralized learning algorithm which enjoys low computational complexity and communication cost among the workers. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place. Finally, we experimentally illustrate the time complexity and convergence behavior of the algorithm.

关键词： decentralized learning neural network ADMM communication network

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Artificial neural networks for distributed optical fiber sensing (Invited)

Artificial neural networks for distributed optical fiber sen...

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Optical Fiber Communications conference and Exhibition (OFC)

作者： Liehr, Sascha DiGOS Potsdam GmbH D-14473 Potsdam Germany

ISBN: (纸本)9781943580866

In recent years, artificial neural networks (ANNs) have been increasingly used to enhance the performance of optical fiber sensors for the distributed measurement of strain and temperature. This paper reviews ANN-based approaches for improved and accelerated raw data processing, denoising, as well as higher-level tasks such as event recognition and classification. (c) 2021 The Author(s)

关键词： Brillouin optical time domain reflectometry neural networks Optical sensing Optical time domain reflectometry Signal processing Strain measurement

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