RBF Neural Network Identifier Based on Optimal Selection Cluster Algorithm and PSO Algorithm and its Application

Duan, Xiangjun; Wang, Yanqin

doi:10.1109/icicta.2011.222

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…In this scheme, the accuracy of the prediction model is directly determined by the number of radial basis functions in the hidden layer of the neural network. Under normal circumstances, the more basic functions, the higher the accuracy of the algorithm and then the more iterations of the algorithm [18]. Therefore, the key to this prediction model is that the count of hidden layer centers needs to be determined.…”

Section: The Center Number Of the Rbf Neural Network Ismentioning

confidence: 99%

“…According to the subtractive clustering algorithm, suppose m centers are determined and each center is k-dimensional, then the position of the particle is m × ðk + 1Þ-dimensional, the velocity of it is also m × ðk + 1Þ-dimensional, σ i represents the width of the i basis function, and f i is the fitness of the i individual. Formulas (17) and (18) show the fitness function: A random particle swarm is generated by the PSO algorithm, and each particle is given a random velocity. During the flight, the speed of the particles is dynamically adjusted by the pulling force of their own and companions' flight experience, and the whole group can fly to a better search area [22].…”

Section: Implementation Of Improved Pso In the Rbfmentioning

confidence: 99%

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RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

Zhu

Wang

Zheng

et al. 2021

Wireless Communications and Mobile Computing

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On the basis of the chaotic features of the frequency hopping signal, frequency band prediction for frequency hopping signal can enhance the interference effect of the signal greatly. However, poor prediction accuracy often limits its development in the military field. Therefore, for the sake of enhancing the frequency band prediction accuracy of frequency hopping signal, this paper studies the radial basis function (RBF) neural network frequency hopping signal frequency band prediction model based on the gradient descent method and improved the particle swarm optimization algorithm, respectively. The former uses a step-by-step algorithm to optimize the center value and weight so that the network can find the most suitable initial state. Then, the clustering selection optimization algorithm is employed to optimize the central value. In addition, it optimizes the weight by using a gradient descent method of the optimal learning rate. The latter optimizes the structure of the RBF neural network through the combination of the subtractive clustering algorithm and improved the particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the gradient RBF algorithm model performs better in terms of accuracy, but time efficiency is lower, while the PSO-RBF algorithm has better time efficiency.

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Section: The Center Number Of the Rbf Neural Network Ismentioning

confidence: 99%

Section: Implementation Of Improved Pso In the Rbfmentioning

confidence: 99%

RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

Zhu

Wang

Zheng

et al. 2021

Wireless Communications and Mobile Computing

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show abstract

“…The weights between the Network output and output layer are linear which can be get using the least square method. Therefore, the determinations of c , b as well as n are the key to establish RBF neural network [4] .…”

Section: Rbf Network Optimal Control Based On Pso Algorithmmentioning

confidence: 99%

The Application of PID Based on Optimized RBF in Thickness Control of Strip Steel

Ren

Deng

et al. 2012

AMR

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The strip thickness control system is difficult to establish an accurate mathematical model, and traditional PID control strategy has a poor adaptive ability, so the effect of control is always not satisfying. According to the problems above, a new control strategy of self-tuning PID controller based on RBF neural network whose parameters are optimized by PSO algorithm is proposed in the paper. The control method integrates advantages of RBF neural network as well as PID controller and good global search capability of PSO algorithm. The simulation results indicate that the method not only improves control performance and dynamic quality, but also has strong self-adapting ability and robustness. It achieved a very good control effect when used in strip thickness control system that proved the correctness and effectiveness of the control method.

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“…The structure is showed in Fig.1. For RBF neural network, the parameters need to be determined can be divided into the following two categories: (1) the basic function center Ciand the length of neural network σi, (2) the connection weights between output layer and hidden layer [4]. The learning difficulty of RBF neural network is the selection and adjustment of these two kinds of parameters.…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Energy Consumption Prediction Model of Public Buildings Based on PSO-RBF

Cao¹,

Huang²

2017

dtcse

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By analyzing thechange characteristics of energy consumption of public buildings in hot summer and cold winter zone, a building energy consumption prediction model based on RBF neural network is established. On this basis, particle swarm optimization is used to optimize our model, and the building energy consumption prediction model based on PSO-RBF is established. This paper applies large numbers of data to construct sample set, trains the prediction model before and after optimization by software, and appliesit to a typical energy consumption prediction example of public buildings. The results show that the building energy consumption prediction model based on PSO-RBF occupies preferable learning ability and predication performance, and achieves the energy consumption prediction of public building accurately.

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RBF Neural Network Identifier Based on Optimal Selection Cluster Algorithm and PSO Algorithm and its Application

Cited by 4 publications

References 3 publications

RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

The Application of PID Based on Optimized RBF in Thickness Control of Strip Steel

Energy Consumption Prediction Model of Public Buildings Based on PSO-RBF

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