Fuzzy Decision Tree with Fuzzy Particle Swarm Optimization Clustering for Locating Users in an Indoor Environment Using Wireless Signal Strength

2018 ◽  
pp. 217-225 ◽  
Author(s):  
Swathi Jamjala Narayanan ◽  
Boominathan Perumal ◽  
Cyril Joe Baby ◽  
Rajen B. Bhatt
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Indoor Environment ◽  
Particle Swarm ◽  
Signal Strength ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Swarm Optimization ◽  
Wireless Signal ◽  
Wireless Signal Strength

scholarly journals Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141772927 ◽  
Author(s):  
Yunzhou Zhang ◽  
Hang Hu ◽  
Wenyan Fu ◽  
Hao Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robot ◽  
Indoor Environment ◽  
Particle Swarm ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Robot Localization ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization ◽  
Minimum Residual

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

scholarly journals Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration

2010 ◽  
Vol 7 (4) ◽  
pp. 859-882 ◽  
Author(s):  
Bae-Muu Chang ◽  
Hung-Hsu Tsai ◽  
Xuan-Ping Lin ◽  
Pao-Ta Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
High Performance ◽  
Impulse Noise ◽  
Median Filter ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Ordered Median ◽  
Mean Filter ◽  
Hybrid Detector

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

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