A conjugate-gradient optimization method invariant to nonlinear scaling

1979 ◽  
Vol 27 (2) ◽  
pp. 221-230 ◽  
Author(s):  
W. R. Boland ◽  
E. R. Kamgnia ◽  
J. S. Kowalik
Keyword(s):  
Conjugate Gradient ◽  
Optimization Method ◽  
Gradient Optimization ◽  
Conjugate Gradient Optimization

A Conjugate Gradient Optimization Algorithm for Wavy Trajectory of Missile

2013 ◽  
Vol 427-429 ◽  
pp. 1368-1371 ◽  
Author(s):  
Zhi Gang Wang ◽  
Ning Zhang
Keyword(s):  
Optimization Algorithm ◽  
Conjugate Gradient ◽  
Optimization Model ◽  
Gradient Optimization ◽  
Penetration Ability ◽  
Specific Formula ◽  
Optimizing Algorithm ◽  
Conjugate Gradient Optimization

Since the traditional parabola trajectory is prone to be head off, wavy trajectory has the flight characteristics of defense penetration ability and increasing range. On the basis of flight characteristics of wavy trajectory missile, a method for flight program angle was designed. A specific formula was given. An optimization model was established. A conjugate gradient optimizing algorithm was used to solve the model and the result is compared with the former trajectory. It shows that optimized wavy trajectory can prove the feasibility of increasing range and valid defense penetration ability.

scholarly journals Hybrid Particle Swarm and Conjugate Gradient Optimization in Neural Network for Prediction of Suspended Particulate Matter

2019 ◽  
Vol 125 ◽  
pp. 25007 ◽  
Author(s):  
Budi Warsito ◽  
Alan Prahutama ◽  
Hasbi Yasin ◽  
Sri Sumiyati
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Conjugate Gradient ◽  
Particle Swarm ◽  
Ambient Air ◽  
Heuristic Optimization ◽  
Swarm Optimization ◽  
Suspended Particulate ◽  
Gradient Optimization ◽  
Conjugate Gradient Optimization

The scope of this research is the use of artificial neural network models and meta-heuristic optimization of Particle Swarm Optimization (PSO) for the prediction of ambient air pollution parameter data at air quality monitoring stations in the city of Semarang, Central Java. The observed parameter is an indicator of ambient air quality, Suspended Particulate Matter (SPM). Based on air quality parameter data in previous times which is a time series data, modeling is done using Neural Networks (NN). Estimation of weights from NN is done using a hybrid method between meta-heuristic and gradient optimization. The meta-heuristic optimization method used is Particle Swarm Optimization (PSO) while the gradient based method is the Conjugate Gradient. Optimization with PSO is done first, then proceed with optimization using the Conjugate Gradient. Four scenarios of iteration selection at the PSO stage are 10, 25, 50 and 100. At the Conjugate Gradient, stage iteration is carried out up to 1000 epohs. The predicted results were compared with the PSOs and Conjugate Gradient respectively. The results show that the hybrid method provides better predictions. The number of iterations needed at the PSO stage is not too much so it is efficient in combining the two methods.

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