New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

2019 ◽  
Vol 33 (7) ◽  
pp. 2335-2356 ◽  
Author(s):  
Sarita Gajbhiye Meshram ◽  
M. A. Ghorbani ◽  
Ravinesh C. Deo ◽  
Mahsa Hasanpour Kashani ◽  
Chandrashekhar Meshram ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Sediment Yield ◽  
Optimization Model ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Neuron Network ◽  
Two Phase ◽  
Swarm Optimization ◽  
New Approach ◽  
Gravitational Search

scholarly journals Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sahazati Md Rozali ◽  
Mohd Fua’ad Rahmat ◽  
Abdul Rashid Husain
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Tracking Error ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Backstepping Controller ◽  
Gravitational Search

This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization (PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.

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