A Novel RBF Neural Network-based Hybrid Technique and Its Applications

Recent studies have demonstrated that Radial Basis Function Neural Networks (RBFNNs), based on traditional methods like gradient descent, frequently produce local approximates after training. This motivation led us to propose an RBFNN-based novel hybrid algorithm that maintains the local and global approximation properties throughout the training process and maintains the balance between these approximation properties. This study introduces the RBFNN-based hybrid particle swarm optimization and cuckoo search with biogeography-based optimization (PSO-CS-BBO) algorithm for global exploration and optimal regions. Several experiments have taken place through six distinct methods for the initialization of RBFNN fitting to perceive the best alignment. Initially, the proposed hybrid was applied to a simple trigonometric function to find the ability of our approach to approximate the function, convergence graph, MSE, and stability analysis. In the final method, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method has been incorporated with our proposed technique to refine the outcome further and improve the convergence to the local optimum. This new hybrid scheme is capable of enhancing the training speed and convergence accuracy. Also, this hybrid-based network has been utilized in different real-world applications to evaluate its problem-solving capabilities, efficacy, and accuracy. The obtained results are rigorously compared with three existing RBFNN-based algorithms. The comparative analysis focused on approximate solutions, the algorithm's convergence, and mean square error (MSE). The proposed results reveal that the RBFNN-based hybrid technique is capable of solving complex, high-dimensional, stochastic, and nonlinear problems.