Wind Power Generation Prediction by Particle Swarm Optimization Algorithm and RBF Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 2099-2102 ◽  
Author(s):  
Rui Fang Liu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Wind Power ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Wind Power Generation ◽  
Swarm Optimization ◽  
Hidden Layer

Wind power generation trend prediction is the important to make the plan on the development of wind power generation. Wind power generation prediction by particle swarm optimization algorithm and RBF neural network in the paper. As the connection weights between the hidden layer and output layer, the centers of radial basis function in hidden layer and the widths of radial basis function in hidden layer have a great influence on the prediction results of RBF neural network,particle swarm optimization which has a great global optimization ability is used to optimize the three parameters including the connection weights between the hidden layer and output layer, the centers of radial basis function in hidden layer and the widths of radial basis function in hidden layer. It is indicated that the hybrid model of particle swarm optimization algorithm and RBF neural network has better prediction ability than BP neural network.

A Non-Destructive Method Based on QPSO-RBF for the Measurement of Sugar Content in Cantaloupe

2012 ◽  
Vol 571 ◽  
pp. 505-509
Author(s):  
Li Li Gao ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Xiao Hui Huang ◽  
Yong Bo Yao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Sugar Content ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Grey Forecasting ◽  
Radial Basis

A nondestructive measurement approach is presented in this paper, which is capable of determining sugar content in cantaloupe from the dielectric property. The approach is based on measured equivalent capacitance and equivalent resistance of the cantaloupe, and on data analysis using quantum-behaved particle swarm optimization (QPSO) and Grey radial basis function (RBF) neural network. First, accumulated generating operation (AGO) in Grey forecasting is used to convert the initial observed data to obtain the accumulated data with strong regularity, which are employed to model and train the radial basis function neural network. Second, it adopted quantum-behaved particle swarm optimization algorithm to train the centers and widths of radial basis function. This model not only prevented the problem that the parameters of neural network are hard to be tuned, but also improved the network precision of prediction. Experimental results revealed that the predictive model as proposed has good predictive effect for the measurement of sugar in cantaloupes.

scholarly journals Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jinna Lu ◽  
Hongping Hu ◽  
Yanping Bai
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Radial Basis

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

scholarly journals Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dong Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Physical Education ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Network System ◽  
Swarm Optimization ◽  
Neural Network System

This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.

PREDICTION OF PARKINSON'S DISEASE TREMOR ONSET USING A RADIAL BASIS FUNCTION NEURAL NETWORK BASED ON PARTICLE SWARM OPTIMIZATION

2010 ◽  
Vol 20 (02) ◽  
pp. 109-116 ◽  
Author(s):  
DEFENG WU ◽  
KEVIN WARWICK ◽  
ZI MA ◽  
MARK N. GASSON ◽  
JONATHAN G. BURGESS ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis ◽  
Deep Brain

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

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