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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131-140 订阅

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A Feature Selection Method Based On Software Defined networks 30

A Feature Selection Method Based On Software Defined Network...

引用

30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Undar, Desdina Kof Pehlivanli, Zeynep Gul Arsal, Ali ULAK Haberlesme AS Sistem Muhendisligi Ankara Turkey

ISBN: (纸本)9781665450928

Software-defined networking is in the midst of information security challenges. In the case that distributed denial of service attacks occur against a software-defined network controller, network traffic data becomes vulnerable because of the overload. In this study, distributed denial of service attacks in software defined network are detected by using machine learning based models with different datasets. First, certain features are obtained on the software-defined network for the dataset under normal conditions and under attack traffic. Subsequently, a new data set was generated by using the proposed hybrid feature selection method on the existing data set. The proposed feature selection method algorithm is trained and tested with logistic regression, artificial neural network, k-nearest neighbors and Naive Bayes models. The results show that the use of the hybrid method has positive impacts on the performance metrics and provides lower processing time.

关键词： software defined network feature selection machine learning logistic regression artificial neural network k-nearest neighbors naive bayes

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A novel artificial neural network trained using evolutionary algorithms for reinforcement learning 9

A novel artificial neural network trained using evolutionary...

引用

9th International conference on neural Information processing

作者： Reddipogu, A Maxwell, G MacLeod, C Simpson, M Robert Gordon Univ Aberdeen AB10 1FR Scotland

ISBN: (纸本)9810475241

This paper discusses the development of a novel Pattern Recognition System using Artificial neural networks (ANNs) and Evolutionary Algorithms for Reinforcement Learning (EARL). The net:work is based on neuronal interactions involved in identification of prey and predator in toads. The distributed neural network (DNN) is capable of recognizing and classifying various features. The lateral inhibition between the output neurons helps the network in the classification process - similar to the gate in gating network. The results obtained are compared with standard neural network architectures and learning algorithms.

关键词： Evolutionary algorithms

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Design of neural network quantizers for a distributed estimation system with communication constraints

Design of neural network quantizers for a distributed estima...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP 98)

作者： Megalooikonomou, V Yesha, Y Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)0780344286

We consider the problem of quantizer design in a distributed estimation system with communication constraints at the channels in the case where the observation statistics are unknown and one must rely on a training set. The method that we propose applies a variation of the Cyclic Generalized Lloyd Algorithm (CGLA) on every point of the training set and then uses a neural network for each quantizer to represent the training points along with their associated codewords. The codeword of every training point is initialized using a regression tree approach. Simulations show that the combined approach i.e. building the regression tree system and using its quantizers to initialize the neural networks provides an improvement over the regression tree approach except in the case of high noise variance.

关键词： Speech communication

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Research on a new method of islanding detection based on lifting wavelet and neural network

Research on a new method of islanding detection based on lif...

引用

International conference on Electronics, Electrical and Control System (EECS)

作者： Xie, Dong Zang, Dajin Gao, Peng Tongling Univ Dept Elect Engn Tongling Peoples R China

At present, the commonly used active and passive islanding detection methods have their own shortcomings, and the islanding detection effect is difficult to meet the requirements. Therefore, a new detection method based on wavelet signal processing and artificial intelligence to identify islanding is proposed in this paper. In this method, the feature quantities required for islanding detection are obtained by wavelet transform and signal processing, and then the feature quantities are identified by neural network to determine whether the islanding is generated in distributed generation *** transformation has a strong ability of signal feature extraction, while the neural network has strong learning and identification abilities, the combination of the both is beneficial to improve the success rate of islanding detection. Simulation verification shows that the new islanding detection method proposed in this paper can detect islanding quickly and accurately, and the performance of islanding detection has been significantly improved.

关键词： distributed generation islanding detection wavelet analysis neural network

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Implementation of the morphological shared-weight neural network (MSNN) for target recognition on the Parallel Algebraic Logic (PAL) computer

Implementation of the morphological shared-weight neural net...

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SPIE conference on Parallel and distributed Methods for Image processing II

作者： Li, HZ Shi, HC Gader, P Keller, J Univ Missouri Dept Comp Engn & Comp Sci Columbia MO 65211 USA

ISBN: (纸本)0819429074

The morphological shared-weight neural network (MSNN) is an effective approach to automatic target recognition. Implementation of the network in parallel is critical for real-time target recognition systems. Although there is significant parallelism inherent in the MSNN, it is a challenge to implement it on an SIMD parallel computer consisting of a large array of simple processing elements. This paper discusses issues related to detection accuracy and throughput in implementing the MSNN on the Parallel Algebraic Logic (PAL) computer.

关键词： target recognition neural network morphology SIMD processor array

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distributed Zero-Order Optimization under Adversarial Noise 35

Distributed Zero-Order Optimization under Adversarial Noise

引用

35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Akhavan, Arya Pontil, Massimiliano Tsybakov, Alexandre B. Ist Italiano Tecnol CSML Genoa Italy CREST ENSAE IP Paris Paris France UCL London England

ISBN: (纸本)9781713845393

We study the problem of distributed zero-order optimization for a class of strongly convex functions. They are formed by the average of local objectives, associated to different nodes in a prescribed network. We propose a distributed zero-order projected gradient descent algorithm to solve the problem. Exchange of information within the network is permitted only between neighbouring nodes. An important feature of our procedure is that it can query only function values, subject to a general noise model, that does not require zero mean or independent errors. We derive upper bounds for the average cumulative regret and optimization error of the algorithm which highlight the role played by a network connectivity parameter, the number of variables, the noise level, the strong convexity parameter, and smoothness properties of the local objectives. The bounds indicate some key improvements of our method over the state-of-the-art, both in the distributed and standard zero-order optimization settings. We also comment on lower bounds and observe that the dependency over certain function parameters in the bound is nearly optimal.

关键词： Gradient methods

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Recursive neural network Paraphrase Identification for Example-based Dialog Retrieval

Recursive Neural Network Paraphrase Identification for Examp...

引用

Annual Summit and conference of Asia-Pacific-Signal-and-Information-processing-Association (APSIPA)

作者： Nio, Lasguido Sakti, Sakriani Neubig, Graham Toda, Tomoki Nakamura, Satoshi Nara Inst Sci & Technol Grad Sch Informat Sci 8916-5 Takayama Cho Ikoma Nara 6300192 Japan

ISBN: (纸本)9786163618238

An example-based dialog model often require a lot of data collections to achieve a good performance. However, when it comes on handling an out of vocabulary (OOV) database queries, this approach resulting in weakness and inadequate handling of interactions between words in the sentence. In this work, we try to overcome this problem by utilizing recursive neural network paraphrase identification to improve the robustness of example-based dialog response retrieval. We model our dialog pair database and user input query with distributed word representations, and employ recursive autoencoders and dynamic pooling to determine whether two sentences with arbitrary length have the same meaning. The distributed representations have the potential to improve handling of OOV cases, and the recursive structure can reduce confusion in example matching.

关键词： interactive systems neural nets query processing OOV database queries arbitrary length sentences data collections dialog-pair database distributed word representations dynamic pooling example matching confusion reduction example-based dialog response retrieval model out of vocabulary database queries recursive autoencoders recursive neural network paraphrase identification recursive structure robustness improvement sentence words user input query Databases Generators Motion pictures neural networks Semantics Syntactics Vectors

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Triple-partition network: Collaborative neural network based on the 'End Device-Edge-Cloud'

Triple-partition Network: Collaborative Neural Network based...

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IEEE Wireless Communications and networking conference (WCNC)

作者： Gao, Zhipeng Miao, Dong Zhao, Langcheng Mo, Zijia Qi, Guangpeng Yan, Liang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Inspur Grp Ltd Jinan 25001 Peoples R China

ISBN: (纸本)9781728195056

The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource constrained distributed frameworks. To address this issue, we propose Triple-partition network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition network on a state-of-art platform and show that trained Triple-partition network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.

关键词： Edge computing distributed computing model End device-edge-cloud Exit points Triple-partition network

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THE distributed ARCHITECTURE FOR LARGE neural networkS (DISTAL) OF THE HUMANOID ROBOT MYON

THE DISTRIBUTED ARCHITECTURE FOR LARGE NEURAL NETWORKS (DIST...

引用

International conference on neural Computation Theory and Applications

作者： Hild, Manfred Thiele, Christian Benckendorff, Christian Humboldt Univ Neurorobot Res Lab Dept Comp Sci Berlin Germany

ISBN: (纸本)9789898425843

Humanoid robots are complex systems that require considerable processing power. This applies both for low-level sensorimotor loops, as well as for image processing and higher level deliberative algorithms. We present the distributed architecture DISTAL which is able to provide the processing power of large neural networks without relying on a central processor. The architecture successfully copes with runtime-metamorphoses of modular robots, such as the humanoid robot MYON, the body parts of which can be detached and reattached during runtime. We detail the implementation of DISTAL on 32-bit ARM RISC processors, describe the underlying neural byte-code (NBC) of neurons and synapses, and also depict the graphical application software BRAINDESIGNER which releases the user from program coding.

关键词： neural network architectures distributed systems Modular implementations Humanoid robotics

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Vector Operations in neural networks Computations

Vector Operations in Neural Networks Computations

引用

14th ACIS International conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed Computing (SNPD)

作者： Ishii, Naohiro Deguchi, Toshinori Kawaguchi, Masashi Sasaki, Hiroshi Aichi Inst Technol Toyota 47003 Japan Suzuka Coll Technol Mie Japan Gifu Natl Coll Technol Gifu Japan Fukui Univ Technol Fukui Japan

ISBN: (纸本)9780769550053

To make clear the mechanism of the visual movement is important in the visual system. The problem is how to perceive vectors of the optic flow in the network. First, the biological asymmetric network with nonlinearities is analyzed for generating the vector from the point of the network computations. The results are applicable to the V1 and MT model of the neural networks in the cortex. The stimulus with a mixture distribution is applied to evaluate their network processing ability for the movement direction and its velocity, which generate the vector. Second, it is shown that the vector is emphasized in the MT than the V1. The characterized equation is derived in the network computations, which evaluates the vector properties of processing ability of the network. The movement velocity is derived, which is represented in Wiener kernels. The operations of vectors are shown in the divisive normalization network, which will create curl or divergence vectors in the higher neural network as MST area.

关键词： neural network vector operation visiual pathway asymmetric neural network nonlinearity in vision

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