Associative Neural Network

The traditional synthetic feed-forward neural network (ANN) is a bleak vision. This means that after completing the training, all the information about the input pattern is stored in neural network weight and input data is not required, i.e. there is no clear storage of any presented instance in the system. On the contrary, the closest-to-neighbors (KNN) (for example, Dasherty, 1991), perjonwindow regressions (e.g., Hurdle, 1990), etc., represent the memory-based approach. These approaches keep the whole database of memory in memory and their predictions are based on some local projections of stored examples. Neural networks can be considered as a global model, while the other two approaches are generally considered to be local models For example, consider the problem of multiplexing function approximation, i.e. finding mapping RM => RN from given set of sample points. For simplicity, we assume that n = 1 A global model input provides a good estimate of the data space RM's global metric. However, if the work analyzed, F, is very complex, then there is no guarantee that all details of F, i.e., will be represented in its good structure. Thus, the global model can be insufficient because it does not mainly describe the entire state's place due to the high bias of the global model in certain areas of space. ANN variation can also contribute to poor performance of this method (Gemman et al., 1992). However, variation can be reduced by analyzing the large number of variation networks, i.e. using the artificial neural network ensemble (ANNE), and for example, taking a general average of all networks in the form of the last model. The problem of ANN's bias can not be easily addressed, for example, using such a large nervous network, such networks can fall into the local minimum and thus there may still be enough bias.