Hierarchical Architecture of Multilayer Perceptrons for Performance Improvement
Based on the theoretical results that multi layer feedforward neural networks with enough hidden nodes are universal approximators, we usually use three layer MLP`s(multi layer perceptrons) consisted of input, hidden, and output layers for many application problems. However, this conventional three layer architecture of MLP shows poor generalization performance in some applications, which are complex with various features in an input vector. For the performance improvement, this paper proposes a hierarchical architecture of MLP especially when each part of inputs has a special information. That is, one input vector is divided into sub vectors and each sub vector is presented to a separate MLP. These lower level MLPs are connected to a higher level MLP, which has a role to do a final decision. The proposed method is verified through the simulation of protein disorder prediction problem.