A Novel Fuzzy Learning Model based on Forgetting Factor

Biological observations, indicate that amnesia is an integral part of the human learning system. Thus, amnesia in learning algorithms is not necessarily destructive and can be constructive. In implementation, due to space constraints and the number of neurons, a limited number of training patterns can be taught to the network. Consequently, to be able to obtain long-term learning capability, it must possess a kind of forgetting mechanism to make space for new learning patterns. Thus, a type of forgetting mechanism similar to the function of the human brain is necessitated. The need for a forgetting mechanism is more acute in online training. Amnesia is modeled as the loss of information from memory. In this paper, the ALM, which is one of the most widely used methods, is employed. The selected algorithm models the system based on the distribution of ink drops based on training data. In this method, in all the implementations, the amplitude of the ink drops on the IDS planes remains unchanged, and no amnesia occurs, which is contrary to biological observations. In this work, the forgetting mechanism is added to the presented algorithm, and simulations in the modeling process are investigated.