Abstract - The present work investigates an appropriate
algorithm based on Multilayer Perceptron Neural Network
(MPNN), Apriori association rules and Particle Swarm
Optimization (PSO) models for predicting two significant core
safety parameters; the multiplication factor Keff and the power
peaking factor Pmax of the benchmark 10 MW IAEA LEU
research reactor. It provides a comprehensive analytic method
for establishing an Artificial Neural Network (ANN) with selforganizing
architecture by finding an optimal number of
hidden layers and their neurons, a less number of effective
features of data set and the most appropriate topology for
internal connections. The performance of the proposed
algorithm is evaluated using the 2-Dimensional neutronic
diffusion code MUDICO-2D to obtain the data required for the
training of the neural networks. Simulation results
demonstrate the effectiveness and the notability of the
proposed algorithm comparing with Trainlm-LM, quasi-
Newton (Trainbfg-BFGS), and Resilient Propagation (trainrp-
RPROP) algorithms.