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

Abstract

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.

Rainfall study based on ARIMA-RBF combined model

“…The center value, width, and weights can be updated by gradient descent with adaptive adjustment [16] in the following form.…”

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

“…The RBF neural network is a feedforward neural network [53][54][55] and it is composed of the input layer, hidden layer and output layer [56,57]. RBF is an ideal calculation tool for nonlinear problems (see Figure 6 for details on the RBF structure).…”