Gauss-sigmoid neural network

RBF (radial basis function)-based networks have been widely used because they can learn a strong nonlinear function fast and easily due to their local learning characteristics. Among them, Gaussian soft-max networks have generalization ability better than regular RBF networks because of their extrapolation ability. However, since the RBF-based network has no hidden unit which can represent some global information, the internal representation cannot be obtained. Accordingly even if the knowledge which could be obtained through the previous sets of learning is utilized effectively in the present learning, the network has to learn from scratch. Multi-layered neural networks are able to form the internal representation in the hidden layer through learning. The paper proposes a Gauss-sigmoid neural network for learning with continuous input signals. The input signals are put into a RBF network, and then the outputs of the RBF network are put into a sigmoid-based multi-layered neural network. After learning based on backpropagation, the localized signals from the RBF network are integrated and an appropriate space for the given learning is reconstructed in the hidden layer of the sigmoid-based neural network. Once the hidden space is constructed, both the advantage of the local learning and the global generalization ability can exist together