Supervised learning algorithm based on spike optimization mechanism for multilayer spiking neural networks

Hu, Tiandou; Lin, Xianghong; Wang, Xiangwen; Du, Pangao

doi:10.1007/s13042-021-01500-8

Cited by 9 publications

(8 citation statements)

References 34 publications

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“…In addition, Hu et al constructed a prediction model using random walk and fully verified it on two effective data sets to prove the feasibility of the algorithm when most traditional collaborative filtering methods ignored contextual information such as network location. It is verified that network location is really useful in QoS (quality of service) prediction [13].…”

Section: Literature Reviewmentioning

confidence: 86%

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Kang

Hua

2022

Journal of Sensors

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In order to explore how to realize network mining for associated users, an algorithm of associated user mining based on recommendation system is proposed. This method recommends key technical problems and solutions based on multi-information fusion to explore the research of user network data mining. The data mining algorithm of associated user network based on multi-information fusion is about 35% higher than the previous method. This article is combined with common scoring data to improve the accuracy of algorithm results. The experiment was carried out on the real data set FilmTrust to evaluate the proposed new algorithm, measure the prediction accuracy of the score prediction results, and compare the offline test results to verify the effectiveness of the new algorithm.

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Section: Literature Reviewmentioning

confidence: 86%

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Kang

Hua

2022

Journal of Sensors

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“…Simultaneously from formula (19), formula (21), and formula ( 22), the equation system is written in the matrix form as shown in the following formula:…”

Section: Vibration Under Moving Loadsmentioning

confidence: 99%

“…For different mass ratios μ and parameters β, the authors used the augmented Lagrangian optimization algorithm to optimize the TMDI damping ratio ξ d and frequency ω d , the optimal damping ratio ξ d and the optimal frequency ω d , respectively, as shown in Figures 2 and 3. As the mass ratio increases, the optimal damping ratio ξ d of the TMDI system also increases, while the optimal frequency ω d decreases [19]. At present, the inertial device of TMDI can amplify the physical mass of the damper by 60 It is worth noting that the TMDI system has different characteristics from the traditional TMD system.…”

Section: 1mentioning

confidence: 99%

Application of the Lagrange Equation for Intelligent Sensor Vibration Control for Power Network Monitoring

Cheng

2022

International Transactions on Electrical Energy Systems

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Objective. In order to control the vibration of the beam structure more effectively and improve the safety and availability of the beam structure, an application study of the Lagrange equation for vibration control of smart sensors for power grid monitoring is proposed. The vibration of the beam structure, the displacement of the beam structure under the excitation of seismic acceleration, the response analytical electrical formula, and the displacement response formula of the beam structure under the action of the kinematic force are deduced. The optimal parameters of the beam-TMDI system are given, and the parameter sensitivity analysis is carried out. Then, the control effect of the TMDI system is studied by numerical analysis, and the vibration reduction effect of the TMDI system and the tuned mass damper (TMD) system is compared. Experimental results show that when the mass ratio μ of the TMDI system and the TMD system are both set to a fixed value of 0.005, and the parameter β of the TMDI system is set to 0, namely β = b = 0 , at this time, the TMDI system degenerates into a TMD system. The TMD natural frequency is 14.179 rad/s and the damping ratio is 0.0432 by the DH optimization method, while the TMD natural frequency is 14.1812 rad/s, and the damping ratio is 0.0436 by the augmented Lagrangian optimization algorithm. Conclusion. The vibration displacement response spectrum of a beam structure obtained by the frequency domain method can effectively reflect electricity in the displacement response of a beam structure. The parameters that minimize the vibration response of the beam structure can be accurately obtained by using the augmented Lagrangian parameter optimization method. The sensitivity of the TMDI system is controlled by the inertial device, and the inertial device has a significant impact on its robustness. The vibration reduction performance of the TMDI system is obviously better than in the conventional TMD systems.

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“…[139] At present, typical learning rules of memristor-based spiking neural networks include: unsupervised learning algorithm spike timing dependent plasticity (STDP) [140] and supervised learning algorithm remote supervised method (ReSuMe). [141] STDP learning rule is to adjust the connection between neurons according to the learning order of neurons. For any two neurons, if the presynaptic neuron pulse starts earlier than the postsynaptic neuron, the connection strength between neurons becomes larger, and vice versa.…”

Section: Learning Rules For Spiking Networkmentioning

confidence: 99%

Integrated Memristor Network for Physiological Signal Processing

Cai

Yue

et al. 2023

Adv Elect Materials

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Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.

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Supervised learning algorithm based on spike optimization mechanism for multilayer spiking neural networks

Cited by 9 publications

References 34 publications

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Application of the Lagrange Equation for Intelligent Sensor Vibration Control for Power Network Monitoring

Integrated Memristor Network for Physiological Signal Processing

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