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A Review of Computing with Spiking Neural Networks
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作者 Jiadong Wu Yinan Wang +2 位作者 Zhiwei Li Lun Lu Qingjiang Li 《Computers, Materials & Continua》 SCIE EI 2024年第3期2909-2939,共31页
Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,exces... Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing. 展开更多
关键词 spiking neural networks neural networks brain-like computing artificial intelligence learning algorithm
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Photonic integrated neuro-synaptic core for convolutional spiking neural network 被引量:2
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作者 Shuiying Xiang Yuechun Shi +14 位作者 Yahui Zhang Xingxing Guo Ling Zheng Yanan Han Yuna Zhang Ziwei Song Dianzhuang Zheng Tao Zhang Hailing Wang Xiaojun Zhu Xiangfei Chen Min Qiu Yichen Shen Wanhua Zheng Yue Hao 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2023年第11期29-42,共14页
Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions... Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip. 展开更多
关键词 neuromorphic computation photonic spiking neuron photonic integrated DFB-SA array convolutional spiking neural network
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Pattern recognition in multi-synaptic photonic spiking neural networks based on a DFB-SA chip 被引量:2
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作者 Yanan Han Shuiying Xiang +6 位作者 Ziwei Song Shuang Gao Xingxing Guo Yahui Zhang Yuechun Shi Xiangfei Chen Yue Hao 《Opto-Electronic Science》 2023年第9期1-10,共10页
Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuro... Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation. 展开更多
关键词 photonic spiking neural network fabricated DFB-SA laser chip multi-synaptic connection optical computing
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A progressive surrogate gradient learning for memristive spiking neural network
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作者 王姝 陈涛 +4 位作者 龚钰 孙帆 申思远 段书凯 王丽丹 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第6期689-697,共9页
In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spa... In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information.However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research. 展开更多
关键词 spiking neural network surrogate gradient supervised learning memristor cross array
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Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification
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作者 P.Immaculate Rexi Jenifer S.Kannan 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1081-1097,共17页
Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented... Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here. 展开更多
关键词 Medical image classification spiking neural networks computer aided diagnosis medical imaging parameter optimization deep learning
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DSNNs:learning transfer from deep neural networks to spiking neural networks 被引量:3
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作者 Zhang Lei Du Zidong +1 位作者 Li Ling Chen Yunji 《High Technology Letters》 EI CAS 2020年第2期136-144,共9页
Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural netwo... Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets. 展开更多
关键词 DEEP leaning spiking neural network(snn) CONVERT METHOD spatially folded network
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SpikeGoogle:Spiking Neural Networks with GoogLeNet-like inception module 被引量:1
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作者 Xuan Wang Minghong Zhong +4 位作者 Hoiyuen Cheng Junjie Xie Yingchu Zhou Jun Ren Mengyuan Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2022年第3期492-502,共11页
Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work pres... Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin. 展开更多
关键词 GoogLeNet INCEPTION spiking neural networks
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Fast Learning in Spiking Neural Networks by Learning Rate Adaptation 被引量:2
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作者 方慧娟 罗继亮 王飞 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1219-1224,共6页
For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and de... For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN. 展开更多
关键词 spiking neural networks learning algorithm learning rate adaptation Tennessee Eastman process
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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network
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作者 Wenwu Jiang Jie Li +4 位作者 Hongbo Liu Xicong Qian Yuan Ge Lidan Wang Shukai Duan 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第4期225-233,共9页
Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,... Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals.The synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with hardware.Secondly,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this paper.The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required.The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the circuit.Finally,a small SNN was designed based on the mathematical model of MSSN.After the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network. 展开更多
关键词 MEMRISTOR multi-synaptic circuit spiking neuron spiking neural network(snn)
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Efficient hybrid neural network for spike sorting
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作者 Hongge Li Pan Yu Tongsheng Xia 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第1期157-164,共8页
Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike s... Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network (PCAN) and normal boundary response (NBR) self-organizing mapping (SOM) net- work classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals contain- ing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system. 展开更多
关键词 neural network spike sorting implantable microsys-tern.
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Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification
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作者 Nurbaity Sabri Haza Nuzly Abdull Hamed +2 位作者 Zaidah Ibrahim Kamalnizat Ibrahim Mohd Adham Isa 《Computers, Materials & Continua》 SCIE EI 2022年第12期5559-5573,共15页
Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation... Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation.Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back.Currently,detecting the curve of the spine is manually performed,making it a time-consuming task.To overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS.This research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification model.This research identifies the optimal Feature Extraction(FE)methods to reduce computational complexity.The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis problem.The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images.This is then followed by feature extraction,normalization,and classification.The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification.This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images. 展开更多
关键词 Adolescent idiopathic scoliosis evolving spiking neural network lenke type local binary pattern PHOTOGRAMMETRY
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Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data
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作者 Tielin Zhang Yi Zeng Bo Xu 《International Journal of Intelligence Science》 2015年第2期102-111,共10页
The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and r... The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence. 展开更多
关键词 neural network Structure PREDICTION SPIKE PREDICTION Time-Order STRATEGY CO-OCCURRENCE STRATEGY SPREADING ACTIVATION
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Accurate and efficient floor localization with scalable spiking graph neural networks
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作者 Fuqiang Gu Fangming Guo +6 位作者 Fangwen Yu Xianlei Long Chao Chen Kai Liu Xuke Hu Jianga Shang Songtao Guo 《Satellite Navigation》 SCIE EI CSCD 2024年第1期191-206,共16页
Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poo... Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization. 展开更多
关键词 Indoor positioning Deep learning Floor localization spiking neural networks Graph neural networks
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Memristor-based spiking neural networks:cooperative development of neural network architecture/algorithms and memristors
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作者 Huihui Peng Lin Gan Xin Guo 《Chip》 EI 2024年第2期62-78,共17页
Inspired by the structure and principles of the human brain,spike neural networks(SNNs)appear as the latest generation of artificial neural networks,attracting significant and universal attention due to their remarkab... Inspired by the structure and principles of the human brain,spike neural networks(SNNs)appear as the latest generation of artificial neural networks,attracting significant and universal attention due to their remarkable low-energy transmission by pulse and powerful capability for large-scale parallel computation.Current research on artificial neural networks gradually change from software simulation into hardware implementation.However,such a process is fraught with challenges.In particular,memristors are highly anticipated hardware candidates owing to their fastprogramming speed,low power consumption,and compatibility with the complementary metal–oxide semiconductor(CMOS)technology.In this review,we start from the basic principles of SNNs,and then introduced memristor-based technologies for hardware implementation of SNNs,and further discuss the feasibility of integrating customized algorithm optimization to promote efficient and energy-saving SNN hardware systems.Finally,based on the existing memristor technology,we summarize the current problems and challenges in this field. 展开更多
关键词 Spike neural networks HARDWARE MEMRISTOR Algorithm Cooperative development
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基于灰度纹理特征提取和CS-SNN的双初级永磁同步直线电机退磁故障诊断研究 被引量:5
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作者 刘铄 宋俊材 +2 位作者 陆思良 吴先红 丁伟 《中国电机工程学报》 EI CSCD 北大核心 2023年第16期6464-6473,共10页
引入一种基于图像形态学纹理特征提取与布谷鸟搜索优化脉冲神经网络(cuckoo search-spiking neural network,CS-SNN)算法相结合的方法,以解决双初级永磁同步直线电机(dual primary permanent magnet synchronous linear motor,DPPMSLM)... 引入一种基于图像形态学纹理特征提取与布谷鸟搜索优化脉冲神经网络(cuckoo search-spiking neural network,CS-SNN)算法相结合的方法,以解决双初级永磁同步直线电机(dual primary permanent magnet synchronous linear motor,DPPMSLM)退磁故障精细定量化诊断识别的问题。首先,根据DPPMSLM拓扑结构约束,通过有限元仿真提取电机气隙空间中三线磁密信号作为有效故障信号;其次,引入图像纹理分析的方法,将一维数据信号映射为二维灰度图像,再采用伽马矫正和边缘提取技术增强图像信息,以提取图像纹理特征组成故障特征向量;然后建立两级CS-SNN分类器实现退磁故障位置类型和严重程度的精确诊断分类;最后,通过退磁样机制作和实验平台验证,提出的新方法能够准确识别DPPMSLM退磁故障位置和严重程度,并具有良好的鲁棒性,是一种有效可行的方法。 展开更多
关键词 双初级永磁同步直线电机 退磁故障诊断 图像纹理分析 故障特征向量 布谷鸟搜索优化脉冲神经网络
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一种基于IGWO-SNN的光伏出力短期预测方法 被引量:12
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作者 董志强 郑凌蔚 +2 位作者 苏然 武浩 罗平 《电力系统保护与控制》 EI CSCD 北大核心 2023年第1期131-138,共8页
光伏出力短期预测对于电网或微电网的能量管理和优化调度具有重要意义。构建了一种基于改进灰狼学习算法(improved grey wolf optimization,IGWO)的脉冲神经网络(spiking neural network,SNN),并将其应用到光伏出力短期预测中。首先,利... 光伏出力短期预测对于电网或微电网的能量管理和优化调度具有重要意义。构建了一种基于改进灰狼学习算法(improved grey wolf optimization,IGWO)的脉冲神经网络(spiking neural network,SNN),并将其应用到光伏出力短期预测中。首先,利用灰色关联分析法选取相似日。然后,提出一种IGWO算法用于SNN模型训练,通过引入基于三角函数规律变化的非线性下降收敛因子和动态权重更新策略,提升SNN的编码和预测的性能。最后,利用实证系统对所提方法进行了评估,并与其他3种模型进行了对比研究。结果表明,所提方法预测性能提升明显。 展开更多
关键词 光伏出力短期预测 脉冲神经网络 改进灰狼优化算法 收敛因子 动态权重更新策略
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基于蜂群优化的Spiking神经网络模型研究与评估 被引量:2
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作者 马韦伟 郑勤红 刘珊珊 《计算机科学》 CSCD 北大核心 2023年第8期221-225,共5页
为提高Spiking神经网络的训练能力,以多标签分类问题作为研究切入点,采用蜂群算法进行模型优化。基于Spiking理念的神经网络模型有多种,文中选择概率Spiking神经网络(Probabilistic Spiking Neural Network,PSNN)进行多标签分类。首先,... 为提高Spiking神经网络的训练能力,以多标签分类问题作为研究切入点,采用蜂群算法进行模型优化。基于Spiking理念的神经网络模型有多种,文中选择概率Spiking神经网络(Probabilistic Spiking Neural Network,PSNN)进行多标签分类。首先,建立概率Spiking神经网络分类模型,通过点火时间序列进行编码,触发脉冲响应实现数据传递;然后,利用Spiking神经网络的权重、动态阈值、遗忘参数等构建蜂群,并以多标签分类准确率作为人工蜂群(Artificial Bee Colony,ABC)算法的适应度函数,从而通过不断更新蜂群个体适应度值来获得最优个体;最后,以最优参数完成概率Spiking神经网络的多标签分类。实验结果表明,通过合理设置蜂群个体规模及蜜源搜索范围,ABC-PSNN算法能够获得较高的多标签分类准确率。相比其他Spiking神经网络模型和常用多标签分类算法,ABC-PSNN算法具备更高的分类准确率和稳定性。 展开更多
关键词 spiking神经网络 概率spiking神经网络 蜂群算法 多标签分类 脉冲响应
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面向声呐图像水下目标分类的类脑SNN研究 被引量:1
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作者 刘扬 田猛 +2 位作者 曹珂境 王瑞毅 赵伟 《计算机工程与应用》 CSCD 北大核心 2023年第10期204-212,共9页
声呐图像被广泛应用于复杂海况的水下救援和海底探测中,长时的人工搜索极易造成视觉疲劳而错失目标。无人潜航器可大幅降低搜索工作量和主观误差,但这取决于无人自主系统的能效和自动分类性能。卷积神经网络的训练和推理需要比较高的能... 声呐图像被广泛应用于复杂海况的水下救援和海底探测中,长时的人工搜索极易造成视觉疲劳而错失目标。无人潜航器可大幅降低搜索工作量和主观误差,但这取决于无人自主系统的能效和自动分类性能。卷积神经网络的训练和推理需要比较高的能耗,难以在无人潜航器的移动环境下部署和应用,而且声呐图像训练数据稀少和样本不平衡也增加了模型训练的难度。脉冲神经网络通过二进制离散的时序脉冲信号可以避免卷积神经网络中高昂的乘法计算代价,具有低能耗和高精度的特性。构建了可用于合成孔径声呐图像分类的浅层脉冲神经网络,设计了一种基于脉冲神经网络的小样本水下目标分类算法。采用基于风格迁移的模拟声呐图像生成方法和加权随机采样方法,缓解了声呐图像训练数据稀少和样本不平衡问题。实验表明,在声呐图像样本稀少和不平衡的情况下,算法的分类准确率高于ResNet50、VGG19和MobileNet V2等架构的卷积神经网络,达到91.11%。计算复杂度和能耗分析也表明,脉冲神经网络相比于卷积神经网络具有很大优势。脉冲神经网络是研究和实现类脑计算非常合适的模型,可满足无人水下航行器的移动计算需求,该研究对实现无人自主设备的智能应用具有先进的技术优势。 展开更多
关键词 脉冲神经网络 近似求导的反向传播算法 合成孔径声呐图像 侧扫声呐 水下目标分类
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基于SNN-LSTM的小样本数据下轴承故障诊断方法 被引量:6
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作者 吕云开 武兵 李聪明 《机电工程》 CAS 北大核心 2023年第1期62-68,共7页
基于深度学习的故障诊断方法的实现,需要用到大量的、有标注的训练样本,而在小样本数据下,采用这些方法会产生模型欠拟合问题,同时获得的分类准确率也较低。为了解决上述问题,提出了一种小样本数据下结合孪生神经网络(SNN)与长短时记忆... 基于深度学习的故障诊断方法的实现,需要用到大量的、有标注的训练样本,而在小样本数据下,采用这些方法会产生模型欠拟合问题,同时获得的分类准确率也较低。为了解决上述问题,提出了一种小样本数据下结合孪生神经网络(SNN)与长短时记忆网络(LSTM)的轴承故障诊断方法。首先,以一对带有正负标签的原始振动信号样本作为诊断方法的输入,采用比较二者相似度的方法,扩充了训练样本个数;然后,采用共享提取样本对特征网络参数的方法,完成了SNN的搭建过程;使用卷积层、池化层及LSTM层提取了原始振动信号的特征,通过计算二者之间的曼哈顿距离,判断输入样本对的相似度,对不同状态下的轴承完成了分类;最后,为了验证基于SNN-LSTM的故障诊断方法在轴承故障诊断中的有效性,通过轴承故障诊断实验,采集了在不同转速、不同状态下的轴承振动信号数据。研究结果表明:当样本数量仅为140个,采用基于SNN-LSTM的故障诊断方法的准确率达到80.57%,相比于深度学习经典方法,在小样本数据下采用该方法具有更高的诊断准确率。 展开更多
关键词 深度学习 孪生神经网络 长短时记忆网络 训练样本 模型欠拟合 分类准确率 曼哈顿距离
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Analysis and Neural Networks Modeling of Web Server Performances Using MySQL and PostgreSQL
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作者 Fontaine Rafamantanantsoa Maherindefo Laha 《Communications and Network》 2018年第4期142-151,共10页
The purpose of this study is to analyze and then model, using neural network models, the performance of the Web server in order to improve them. In our experiments, the parameters taken into account are the number of ... The purpose of this study is to analyze and then model, using neural network models, the performance of the Web server in order to improve them. In our experiments, the parameters taken into account are the number of instances of clients simultaneously requesting the same Web page that contains the same SQL queries, the number of tables queried by the SQL, the number of records to be displayed on the requested Web pages, and the type of used database server. This work demonstrates the influences of these parameters on the results of Web server performance analyzes. For the MySQL database server, it has been observed that the mean response time of the Web server tends to become increasingly slow as the number of client connection occurrences as well as the number of records to display increases. For the PostgreSQL database server, the mean response time of the Web server does not change much, although there is an increase in the number of clients and/or size of information to be displayed on Web pages. Although it has been observed that the mean response time of the Web server is generally a little faster for the MySQL database server, it has been noted that this mean response time of the Web server is more stable for PostgreSQL database server. 展开更多
关键词 Web SERVER PERFORMANCES neural network Database SERVER MYSQL POSTGRESQL APACHE Mean Response Time APACHE Benchmark snnS
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