Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

Radial basis function neural networks trained with gradient descending algorithms offer effective structures for both shallow and deep networks. Researchers have explored the use of particle swarm optimization algorithms as an alternative to traditional gradient-based methods, such as error correction, to optimize these networks. A recent study proposes a multi-column RBF neural network using adaptive and non-adaptive particle swarm optimization, which can improve accuracy by selecting optimal hidden units¹. This approach allows for a more efficient and effective training process, particularly in complex networks. The use of particle swarm optimization algorithms can provide a more robust and flexible training method, especially when dealing with large datasets. This matters to practitioners because it offers a new tool for optimizing neural networks, which can be crucial in applications where accuracy and efficiency are critical, such as in cybersecurity and threat detection.