2.8.10.   Topology Optimization by Growing Neural Network Algorithms

An interesting algorithm for building non-uniform optimized neural network topologies was initially proposed by Vinod et al. [125]. The algorithm starts with a feedforward backpropagation neural network, which has no hidden neurons and no links ("empty network"). The algorithm grows the network by adding one neuron at a time. The neuron is connected to one output neuron and to two other neurons whereby these links are selected on the basis of the maximum estimated error decrease for the calibration data. The insertion of the neurons is stopped when a prescribed error has been reached. It was demonstrated that each growing step does improve the calibration error. It was also shown that the algorithm is able to approximate complex continuous functions (like a sinus wave) using very small networks.