An Optimally Configured HP-GRU Model Using Hyperband for the Control of Wall Following Robot

Published on Mar 10, 2021
· DOI :10.31763/IJRCS.V1I1.281
Abdul Rehman Khan2
Estimated H-index: 2
(PIEAS: Pakistan Institute of Engineering and Applied Sciences),
Ameer Tamoor Khan3
Estimated H-index: 3
(PolyU: Hong Kong Polytechnic University)
+ 1 AuthorsSunila Bakhsh (Balochistan University of Information Technology, Engineering and Management Sciences)
In this paper, we presented an autonomous control framework for the wall following robot using an optimally configured Gated Recurrent Unit (GRU) model with the hyperband algorithm. GRU is popularly known for the time-series or sequence data, and it overcomes the vanishing gradient problem of RNN. GRU also consumes less memory and is computationally more efficient than LSTMs. The selection of hyper-parameters of the GRU model is a complex optimization problem with local minima. Usually, hyper-parameters are selected through hit and trial, which does not guarantee an optimal solution. To come around this problem, we used a hyperband algorithm for the selection of optimal parameters. It is an iterative method, which searches for the optimal configuration by discarding the least performing configurations on each iteration. The proposed HP-GRU model is used on a dataset of SCITOS G5 robots with 24 sensors mounted. The results show that HP-GRU has a mean accuracy of 0.9857 and a mean loss of 0.0810, and it is comparable with other deep learning algorithms.
📖 Papers frequently viewed together
1 Author (Ali H. Mirza)
1 Citations
3 Citations
Cited By3
#1Ameer Tamoor Khan (PolyU: Hong Kong Polytechnic University)H-Index: 3
#2Abdul Rehman Khan (PIEAS: Pakistan Institute of Engineering and Applied Sciences)H-Index: 2
view all 6 authors...
Abstract null null The Photovoltaic generation inherits the instability due to the variability and non-availability of solar irradiation at times. Such unstable generation will cause grid management, planning, and operation issues. Researchers have proposed several classical and advanced algorithms to forecast the power generation of photovoltaic plants to avoid such unsuitability issues. Artificial Neural Networks advancement has pushed them in power forecasting, ranging from yearly to hourly p...