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

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.

A Conjugate Gradient Optimization Algorithm for Wavy Trajectory of Missile

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.

Acanthopanax senticosus Harms Intelligence Quality Assessment Based on Core Data

According to the traditional morphological classification divide the quality of traditional Chinese medicine Acanthopanax Senticosus Harms into recommended products non-recommended products and shoddy goods distribute the chromatogram data Acanthopanax Senticosus Harms obtained in the condition of standard inspect and abstract main data and get the great peaks of linear independent vectors. Get Acanthopanax Senticosus Harms every clustering centre data with liner reduction PSO and Conjugate gradient optimization. Make Acanthopanax Senticosus Harms criterion data as Gamma distribution and calculate similar by the sample data Normal distribution and ensure the finial similar by angle cosine. That will use lada integral and upper gauss interpolation. It need to calculate the similar between unknown sample and the common pattern of corresponding sample when analyze unknown sample. Make appraise classify and evaluate to unknown sample and make good identification to counterfeit medofenoxate for example gracilistylus koxmo hoo column5 plus fine.