Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Abstract

Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

Sensing the material by minimally invasive surgery grasper

“…In addition, the smart graspers can potentially be used as educational tools, surgical simulators as well as in Tele-Robotic surgery. Utilizing Artificial Intelligence (AI) techniques such as Neural Networks [12][13][14][15][16][17][18][19] , Fuzzy logic 20 , Ring Probabilistic Logic Neuron 21,22 can be utilized to model the generated data by sensing the material in minimally invasive surgery, and to get the reliable data the AI models can be optimized using Genetic Algorithm 23,24 , Particle Swarm 21 and other AI optimization techniques.…”

A Case Study on Computer-Based Analysis of the Stochastic Stability of Mechanical Structures Driven by White and Colored Noise: Utilizing Artificial Intelligence Techniques to Design an Effective Active Suspension System

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“…In 2016, Azizi et al [15,22] utilized RPLNN structure as an optimizer tool to optimize weightless neural networks and RFID networks. Later, the RPLNN structure has been developed and utilized as a part of the hybrid optimizers [13,14,16,24,25].…”

Applications of Artificial Intelligence Techniques in Optimizing Drilling

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