A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

“…The parameters of model can be optimized simultaneously by LMM and LSM. The SNPOM is one of these method which divides search space into two subspaces [33,34]. The SNPOM can accelerate the computational convergence of the parameter optimization in RBF network.…”

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

“…An optimization algorithm will attempt to find an optimal choice that satisfies defined constraints and make an optimization criterion (performance or cost index) maximize or minimize [38,40]. Hence, to improve the prediction accuracy and robustness of the RBF network, network parameters (centers, widths and weights) should be simultaneously tuned [32]. Some of the existing algorithms to achieve that are given in [32,36,41,42,43].…”

“…RBF networks have many remarkable characteristics, such as simple network structure, strong learning capacity, better approximation capacities and fast learning speed. The difficulty of applying the RBF networks is in network training which should select and estimate properly the input parameters including centers and widths of the basis functions and the neuron connection weights [32,36,37]. In order to find the most appropriate parameters, an optimization algorithm can be used [38,39].…”

“…Hence, to improve the prediction accuracy and robustness of the RBF network, network parameters (centers, widths and weights) should be simultaneously tuned [32]. Some of the existing algorithms to achieve that are given in [32,36,41,42,43]. Almost all algorithms compute the optimal estimation of the basis function centers by mean of error minimization, i.e., accuracy based on Mean-Square Error (MSE) [36,43,44,45].…”

“…The most popular approach for DOS/DDoS attacks prediction is using Artificial Neural Networks (ANNs) classification [28,29,30]. ANNs have become one of the most vital and valuable tools in solving many complex practical problems [31,32], among which the Radial basis function (RBF) neural networks have been successfully applied for solving dynamic system problems, because they can predict the behavior directly from input/output data [33,34,35]. RBF networks have many remarkable characteristics, such as simple network structure, strong learning capacity, better approximation capacities and fast learning speed.…”