Abstract
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.
Highlights
Computational Intelligence and Neuroscience the disadvantage that algorithm can lead to local minimum
Neural network system for teaching effect evaluation has achieved certain results; through the comparison and research of various neural network algorithms, this study selects RBF algorithm to evaluate the effect of physical education teaching. e hidden node of traditional BP neural network uses the inner product of input pattern and weight vector as the independent variable of activation function, and each tuning parameter has the same influence on the output of the network. e hidden node of RBFNNPSO system uses the distance between the input pattern and the central vector as the independent variable of the function. e farther the input of the neuron is from the center of the radial basis function, the lower the activation degree of the neuron
Local mapping can more accurately reflect the internal relationship between these influences. e innovation of this research is to improve the traditional algorithm by RBFNNPSO algorithm and build a new RBFNN-PSO system model
Summary
Sports Culture Research Base, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China. Is 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. E experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy. E hidden node of traditional BP neural network uses the inner product of input pattern and weight vector as the independent variable of activation function, and each tuning parameter has the same influence on the output of the network. RBF neural network full-interpolation method is divided into Lagrange interpolation, Hermite interpolation, Newton interpolation, spline interpolation, piecewise interpolation, and so on [11]. e interpolation function of complete interpolation method needs to pass through all sample points, and the formula expression is as follows: Input layer X1 X2 Xn
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