Particle Swarm Optimization

Many methods are introduced to accomplish determining the position of emitters with respect to knownposition receivers in indoor localizations. Among them, the direct position determination (DPD) approach advocates using the received signals by all the base stations together in order to estimate the locations in a single step. However, DPD is not very accurate due to the use of a gridding area, the effect of noise, and the multipath phenomenon. In order to improve the DPD performance, we derive an analytic model based on weighted least square estimation that uses simultaneously the effect of delay, Doppler, attenuation, and angle of reception of the signals. In addition, a new approach to define a cost function based on the analytic model is proposed that is optimized by particle swarm optimization (PSO). A combination of the improved DPD and the proposed PSO-based technique is also used to decrease the computation volume and increase the resolution. Finally, the accuracy of the proposed algorithms is investigated by Monte Carlo computer simulation in a wireless local area network. Numerical results show that the localization by PSO, the improved DPD, and previous DPD are more accurate in that order.

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“…According to Eq. (19), each receiver estimates the emitted signal. It is noticed that there are L received signals.…”

Section: The Proposed Positioning Methods Using Psomentioning

confidence: 99%

“…Estimate the emitted signal by using Eq. (19). Average the estimate position of emitter for all time intervals to estimatep et Eq.…”

mentioning

confidence: 99%

Indoor localization of wireless emitter using direct position determination and particle swarm optimization

Hassanhosseini¹,

Taban²,

Abouei³

2018

Turk J Elec Eng & Comp Sci

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“…It generally includes a population of candidate and the particles which are solutions. PSO is initiated by a random cluster of individuals, then search for the optimal solution by updating the generation [18]. In each generation, two values such as Pbest and Gbest are used to update each individual [19].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

et al. 2019

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The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

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“…The most prominent advantage of ELM algorithm is its high efficiency. At the same time, the ELM algorithm overcomes the limitations of local optimization and over fitting typically existing in gradient algorithm (such as BP algorithm), so that the better results are well guaranteed [27].…”

Section: Output Nodementioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

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To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, "adjusting position" or "pushing ball" defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real-time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

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Particle Swarm Optimization

Cited by 118 publications

References 78 publications

Indoor localization of wireless emitter using direct position determination and particle swarm optimization

Indoor localization of wireless emitter using direct position determination and particle swarm optimization

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

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