Constructive algorithm for fully connected cascade feedforward neural networks

Abstract

In this paper, a novel constructive algorithm, named fast cascade neural network (FCNN), is proposed to design the fully connected cascade feedforward neural network (FCCFNN). First, a modified index, based on the orthogonal least square method, is derived to select new hidden units from candidate pools. Each hidden unit leads to the maximal reduction of the sum of squared errors. Secondly, the input weights and biases of hidden units are randomly generated and remain unchanged during the learning process. The weights, which connect the input and hidden units with the output units, are calculated after all necessary units have been added. Thirdly, the convergence of FCNN is guaranteed in theory. Finally, the performance of FCNN is evaluated on some artificial and real-world benchmark problems. Simulation results show that the proposed FCNN algorithm has better generalization performance and faster learning speed than some existing algorithms.