PPNN: a faster learning and better generalizing neural net

It is pointed out that the planar topology of the current backpropagation neural network (BPNN) sets limits to the solution of the slow convergence rate problem, local minima, and other problems associated with BPNN. The parallel probabilistic neural network (PPNN) using a novel neural network topology, stereotopology, is proposed to overcome these problems. The learning ability and the generation ability of BPNN and PPNN are compared for several problems. Simulation results show that PPNN was capable of learning various kinds of problems much faster than BPNN, and also generalized better than BPNN. It is shown that the faster, universal learnability of PPNN was due to the parallel characteristic of PPNN´s stereotopology, and the better generalization ability came from the probabilistic characteristic of PPNN´s memory retrieval rule