An adaptive and efficient particle swarm optimization (PSO)-based learning algorithm to determine neuron centers in the hidden layer of a radial basis neural network (RBNN) is developed in the present work for regression problems. The proposed PSO–RBNN algorithm searches the entire input domain space to discover optimal neuron centers by solving an optimization problem and aims to overcome the limitation of center selection from the training data. The network is built in a sequential manner using optimal neuron centers until some specified criterion is met, and therefore, it exploits the concept of neuron significance during the learning process. The Gaussian function with a constant spread (also known as width) is chosen as the radial basis function for each neuron. To illustrate the effectiveness of the PSO–RBNN algorithm over the orthogonal least squares (OLS) method (a popular learning algorithm under a similar category, which selects the neuron center from training data), numerical simulations for different types of nonlinear problems of varying dimensions and complexities are conducted. Finally, a comparison with multiple existing algorithms for network design is made using available data. The results show that the RBNN architecture developed with the proposed learning algorithm exhibits superior convergence, displays good generalization ability, and requires a smaller number of neurons, resulting in an efficient and compact network architecture.
Arshad Afzal (Thu,) studied this question.