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.

Application of Neural Network Based on Genetic Algorithm to Predict Hypotension Events during Spinal Anesthesia

2012 ◽  
Vol 542-543 ◽  
pp. 1394-1397
Author(s):  
Yu Qiu ◽  
Hai Jun Dai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Optimal Parameters ◽  
Accuracy Requirement ◽  
Artificial Neural Network Ann

An accurate and efficient artificial neural network (ANN) based genetic algorithm (GA) is presented for predicting hypotension during general anesthesia. The genetic algorithm global optimization characteristics are used to optimize the BP neural network weights, 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.

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

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