Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

Sleep Sensor Temperature Prediction Based on Neural Network Training Algorithm with Rational Spline Weight Functions

2015 ◽  
Vol 713-715 ◽  
pp. 1918-1921
Author(s):  
Dai Yuan Zhang ◽  
Hao Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Weight Functions ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Temperature Prediction ◽  
Rational Spline ◽  
Network Training ◽  
Sensor Temperature

In wireless sensor network, it is necessary to make effective prediction of sensor node’s data during its sleep period. In this paper a model of rational cubic spline weight function (SWF) neural network with linear denominator was established for sensor node’s temperature prediction. This kind of rational spline function is denoted by 3/1 rational splines. Then we trained and tested the network, the simulation results showed that, compared to the traditional BP neural network, the training speed is higher and the error is smaller. Therefore the prediction model can effectively predict the sensor’s temperature.

Process Parameters Optimization for Plasma Spraying Nanostructured ZrO2-7%Y2O3 Coating Based on Simulated Annealing Algorithm

2014 ◽  
Vol 1051 ◽  
pp. 12-16
Author(s):  
Bin Yang
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Electric Current ◽  
Plasma Spraying ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Simulated Annealing Algorithm ◽  
Gas Pressure ◽  
Annealing Algorithm

Process parameters of nanostructured ZrO2-7%Y2O3 coating during plasma spraying on the properties of the coating was optimized based on simulated annealing algorithm. BP neural network was applied to compute fitness of simulated annealing algorithm. A BP neural network model was built, four process parameters were input , the parameters included spraying distance, spraying electric current, primary gas pressure and secondary gas pressure, bonding strength of coating was output. Network was trained by orthogonal test data. Process parameters of coating were optimized by simulated annealing algorithm. The results show that maximal bonding strength of coating is 43.0377MPa. Process parameters for plasma spraying nanostructured ZrO2-7%Y2O3 coating are spraying distance of 80mm, spraying electric current of 977.0283A, primary gas pressure of 0.3046MPa and secondary gas pressure 0.9886MPa.

Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

2014 ◽  
Vol 644-650 ◽  
pp. 1954-1956
Author(s):  
Run Ya Li ◽  
Xiang Nan Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Power Load ◽  
Bp Neural Network Model

The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.

scholarly journals A Thresholding Approach for Pollen Detection in Images Based on Simulated Annealing Algorithm

2019 ◽  
Vol 10 (4) ◽  
pp. 18-36
Author(s):  
Hanane Menad ◽  
Farah Ben-Naoum ◽  
Abdelmalek Amine
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
High Efficiency ◽  
Pollen Grains ◽  
Microscopic Images ◽  
The Neural Network ◽  
Pollen Species ◽  
Annealing Algorithm ◽  
Fully Connected

Melissopalynology is a field that studies pollen grain origins to identify their species. It consists of studying either the chemical composition of each grain, or their shapes using microscopic images. This paper presents a system of pollen identification based on the microscopic images, it is divided into two parts, first part is the pollen detection using a thresholding method with simulated annealing algorithm. The second step is the pollen classification, in which we used deep convolutional neural network to extract features from the detected pollen grains and represent them in numerical vectors, therefore, we can use these vectors to classify them based on fully connected neural network, SVM or similarity calculation. The obtained results showed a high efficiency of the neural network in which it could recognize 98.07% of the pollen species compared not just to SVM and similarity methods, but also to works from literature.

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