SA-PSO based optimizing reader deployment in large-scale RFID Systems

Tao, Ming; Huang, Shuqiang; Li, Yang; Yan, Min; Zhou, Yuyu

doi:10.1016/j.jnca.2015.02.011

Cited by 39 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The internal circuit of a passive tag is powered up (activated) by a RF signal (an electromagnetic wave), which has a greater power than the threshold of the tag. The powered up tag then starts to reflect back the signal to the antenna of a reader and if this signal has been received by an antenna, it can thus be said that communication between the tag and reader through the antenna has been established and 6 Complexity that tag has been covered [47]. This entire procedure can be summarized using the following formula [13]:…”

Section: Coveragementioning

confidence: 99%

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

View full text Add to dashboard Cite

Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named "hybrid AI optimization technique," to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

show abstract

Section: Coveragementioning

confidence: 99%

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

View full text Add to dashboard Cite

show abstract

“…The results indicate that the proposed PSO-BP can provide better predictions than a traditional method. In addition, Tao et al [27] proposed an improved particle swarm method (SA-PSO), which can restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm. The simulation results show that the addressed SA-PSO algorithm can solve a problem of multi-objective combination optimization.…”

Section: Applications Of Soft Computing Techniques To Indoor Positionmentioning

confidence: 99%

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

Information

View full text Add to dashboard Cite

Location-based services (LBS) have long been recognized as a significant component of the emerging information services. However, the localization cost and the performance of algorithm still need to be optimized. In the study, an improved particle swarm optimization algorithm based on a feed-forward neural network (IMPSO-FNN) combined with RFID sensors is proposed, which can achieve the best indoor positioning location and overcome the problems effectively. In IMPSO-FNN, an improved PSO algorithm (IMPSO) is developed to determine the optimal connecting weights and markedly optimize the network parameters and structural parameters for the FNN, and then an optimal location prediction model is established by the IMPSO-FNN. To avoid the interference of environmental noise for the experimental data, some preprocessing methods are used during the positioning process. The computational results for learning two continuous functions show that the proposed positioning algorithm has a faster convergence rate and higher generalization performance. The model evaluation results also verify that the proposed positioning method really is superior to other algorithms in terms of the learning ability, efficiency, and positioning accuracy.

show abstract

“…By introducing the metropolis rule of simulated annealing algorithm, M.Tao et al [13] proposed an improved particle swarm algorithm(SA-PSO) to solve RFID network planning, which can restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm.…”

Section: Hybri D Psg Algorithmsmentioning

confidence: 99%

Survey of particle swarm optimization algorithm and its applications in antenna circuit

Qianying

2015

2015 IEEE International Conference on Communication Problem-Solving (ICCP)

View full text Add to dashboard Cite

With the development of intelligent algorithms, particle swarm optimization (PSO) has become the hot spot for the study on objective optimization in recent years. PSO has many successful scientific and engineering applications. In this paper, a detail review of the basic concepts of PSO is provided. Several important ways to improve PSO is presented. Moreover, the theoretical analyses on PSO are provided. Finally, the applications in antenna circuit of PSO and its future challenging field are also pointed out in the remainder of this paper.

show abstract

SA-PSO based optimizing reader deployment in large-scale RFID Systems

Cited by 39 publications

References 21 publications

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Survey of particle swarm optimization algorithm and its applications in antenna circuit

Contact Info

Product

Resources

About