An Efficiency Algorithm for SDCP Program

2014 ◽  
Vol 971-973 ◽  
pp. 1533-1536
Author(s):  
Ning Xiao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Hybrid Intelligent Algorithm ◽  
Random Simulation ◽  
Training Samples ◽  
Stochastic Particle

For more effectively solving SDCP,in the paper,using BP neural networks to approximate chance function,training samples are produced by random simulation,and a hybrid intelligent algorithm for SDCP combined stochastic particle swarm optimization and BP neural network is proposed.The experimental results show that the algorithm is more preferable.

Predict Hypotension Events during Spinal Anesthesia Based on Particle Swarm Optimization Neural Networks

2012 ◽  
Vol 569 ◽  
pp. 733-736
Author(s):  
Hai Jun Dai ◽  
Yu Qiu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Particle Swarm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Accuracy Requirement

Neural network model based on particle swarm optimization (PSO) was established for predicting hypotension during general anesthesia. The BP neural network parameters optimized by pso, and learning samples are trained and modeled by BP neural network with optimal parameters. The simulation experiment is carried out with MATLAB. The result indicated that the model forecasting results are close with the actual results and meet the accuracy requirement to General Anesthesia.

scholarly journals An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Nurbs Surface ◽  
Improved Method ◽  
Swarm Optimization

Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

2018 ◽  
pp. 215-244
Author(s):  
Goran Klepac
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Recommendation System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Combination ◽  
Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

Research of Intelligent Control of Traffic Signal

2014 ◽  
Vol 644-650 ◽  
pp. 107-111
Author(s):  
Xiang Li ◽  
Jin Song Du ◽  
Jing Tao Hu ◽  
Xin Bi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Intelligent Control ◽  
Traffic Signal ◽  
Future Research ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Multi Agent

At present, in the field of intelligent control of traffic signal, most of scholars at home and abroad use fuzzy control and intelligent algorithm, such as genetic algorithm, ant colony optimization, particle swarm optimization, multi-agent, artificial neural networks, fuzzy method etc. This paper summarizes and analyzes these algorithms, points out the problems and shortcomings in the present research, puts forward the direction and trend in the future research. These works have certain directive significance to the research and development of intelligent control of traffic signal.

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