Self-organizing neural networks using adaptive neurons

In this paper, we propose a new kind of neural network having modular structure, neural network with adaptive neurons. Each module is equivalent to an adaptive neuron, which consists of a multi-layer neural network with sigmoid neurons. We develop an algorithm by which the network can automatically adjust its complexity according to the given problem. The proposed network is compared with the project pursuit learning network (PPLN), which is a popular modular architecture. The experimental results demonstrate that the proposed network architecture outperforms the PPLN on four regression problems.