In this paper, we present the performance analysis of a fully tuned neural network
trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time
identification of a quadcopter. Radial basis function network (RBF) based on system
identification can be utilised as an alternative technique for quadcopter modelling. To prevent the
neurons and network parameters selection dilemma during trial and error approach, RBF with
EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the
network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s
performance is compared with the minimal resource allocating network (MRAN) training for
1000 input-output pair untrained attitude data. The findings show that the EMRAN method
generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units
compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy
