The projection neural network
Gregg Wilensky, Narbik Manukian · 2003
A novel neural network model, the projection neural network, is developed to overcome three key drawbacks of backpropagation-trained neural networks (BPNN), i.e., long training times, the large number of nodes required to form closed regions for classification of high-dimensional problems and the lack of modularity. This network combines advantages of hypersphere classifiers, such as the restricted Coulomb energy (RCE) network, radial basis function methods, and BPNN. It provides the ability to initialize nodes to serve either as hyperplane separators or as spherical prototypes (radial basis functions), followed by a modified gradient descent error minimization training of the network weights and thresholds, which adjusts the prototype positions and sizes and may convert closed prototype decision boundaries to open boundaries, and vice versa. The network can provide orders of magnitude decrease in the required training time over BPNN and a reduction in the number of required nodes. Theory and examples are given.>