Dynamic behavioral assessment model based on Hebb learning rule

Yin, Yunfei; Yuan, Hailong; Zhang, Beilei

doi:10.1007/s00521-016-2341-5

Cited by 10 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to different learning signals, neuron learning algorithms can be divided into three categories: unsupervised Hebb learning algorithm, supervised Hebb learning algorithm, and supervised Delta learning algorithm. Using a single neuron with supervised Hebb learning to adjust the weight of the PID controller can improve the dynamic characteristics of the system [ 12 ]. In [ 13 ], the single neuron control technique was adopted to dynamically adjust the parameters of a PID controller and was applied to the internet network router to control the length of the buffer queue, so as to improve the stability and throughput of the queue.…”

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

Yang

Chen

Xia

et al. 2018

Sensors

View full text Add to dashboard Cite

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm–neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

show abstract

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

Yang

Chen

Xia

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…3. The characteristic of the algorithm is that the adaptive control function is realized by adjusting the weight coefficients, and the weight coefficient is adjusted according to the supervised Hebb learning rule [9,10]. In order to realize the constraint control of output error and control incremental weighting, with the idea of optimal control, a quadratic performance index is introduced in the adjustment of the weighting coefficient, and the weighting coefficient is adjusted by minimizing the output error and the control incremental weighted square sum.…”

Section: Single Neural Network Pid Methodsmentioning

confidence: 99%

A new method calculating load balance of sliding bearing by using neural network PID algorithm

Wei¹,

Guo²,

Han³

et al. 2018

Vib. proced.

View full text Add to dashboard Cite

Aiming at low efficiency of existing sliding bearing load balance calculation, a new method that based on neural network proportional-integral-derivative (PID) algorithm is proposed for the first time, in which a compound control algorithm combining neural network and PID algorithm is applied. In this new method, the load error is taken as the input of the system, and the eccentricity of the bearing is used as the input of the controller, and the output of the system is the oil film force of the bearing. Comparing with traditional method, calculation results show that: the number of iterations calculated by neural network PID algorithm is less than traditional one and has higher efficiency and stronger adaptability under different loads.

show abstract

“…The present congestion control technique is based on technology that designs network controllers capable of addressing the control requirements of WSN data streams using automated control theory. The WSN was upgraded using Proportional Integration control technology and the bandwidth for repository data in the network was designated as a controlled object [9]. The purpose of PI controllers is to accomplish active range control.…”

Section: Related Workmentioning

confidence: 99%

Design of a Bio Based Approach for Congestion Control in Wireless Sensor Network

Suchitha¹,

Prasanthi²

2021

INDJCSE

View full text Add to dashboard Cite

In wireless sensor networks (WSNs), congestion can occur due to more data traffic. This leads to increased packet delay, loss of packets, less accuracy. Transmission speed is a factor that contributes to the operation of WSN. We presented a mechanism for adjusting transmission speed to solve this problem. In this work, biodiversity-based monitoring system called BatFuzzyBee is presented. It defines WSN bio definitions that use location information to manage congestion. New cluster-based WSNs are also used in this work to reduce packet loss and save energy. BatFuzzyBee can find the best or closest reliable route, when the network has nodes with different transmission ranges with the least power consumption. The proposed BatFuzzyBee is compared to other methods by considering multiple indicators, such as packet delivery ratio, accuracy and energy consumption. The results from the simulation show that the proposed method gives better performance when compared with the other methods. In this way, the effectiveness of the BatFuzzyBee approach is determined.

show abstract

Dynamic behavioral assessment model based on Hebb learning rule

Cited by 10 publications

References 21 publications

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

A new method calculating load balance of sliding bearing by using neural network PID algorithm

Design of a Bio Based Approach for Congestion Control in Wireless Sensor Network

Contact Info

Product

Resources

About