Sigmoidal FFANN’s and the best approximation property
Yogesh Pal Singh, Pravin Chandra · Journal of Interdisciplinary Mathematics · 2005
Feedforward Artificial Neural Networks (FFANN’s) can approximate any continuous function arbitrarily well. Many approximation schemes exist which have this universal approximation property including schemes based on polynomials, wavelets etc. These approximations schemes can be given a FFANN form. Thus, the universal approximation property is not the characteristic of choice for comparison of approximation schemes. The best approximation property is one of the crucial aspects that can be utilised for this purpose. We establish that the functional sets represented by finite sized networks as well as arbitrary sized networks are open. We prove this result for both linear output FFANN’s and sigmoidal output FFANN’s. We also establish the absence of the best approximation properties for these networks and discuss the physical relevance of the results obtained.