language: en

[Reference: Proceedings of the IASTED International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 249-252, 1996.]

Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)

Tim L. Andersen and Tony R. Martinez
Computer Science Department, Brigham Young University,
Provo, Utah 84602
email: tim@axon.cs.byu.edu, martinez@cs.byu.edu

ABSTRACT This paper discusses a method for training multilayer perceptron networks called DMP2 (Dynamic Multilayer Perceptron 2). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The focus of this paper is on the effects of using multiple node types within the DMP framework. Simulation results show that DMP2 performs favorably in comparison with other learning algorithms, and that using multiple node types can be beneficial to network performance.

1 Introduction