Hybrid Evolutionary Decision-Making Model Based on Neural Network and Immune Approaches

Modern decision support systems (DSS) are characterized by processing of large amounts of information in conditions of uncertainty. Therefore, usage of effective methods and models that use various intelligent technologies for parallel processing of information are required. A hybrid decision-making model (DMM) based on a neural network is considered. Its training and evolution are carried out on high-performance systems using the immune clonal selection models and the immune network. The evolution of the model is considered as the task of neural network adaptation. It consists of the procedures of correcting the number of neurons in the hidden layers and the relationships between them, as well as the parameters of the model.