This brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator. Here we study with rates the approximation properties of the "right" sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study the degree of approximation of these operators to the unit operator in the univariate and multivariate cases over bounded or unbounded domains. This is given via inequalities and with the use of modulus of continuity of the involved function or its higher order derivative. We examine the real and complex cases.
For the convenience of the reader, the chapters of this book
are written in a self-contained style.
This treatise relies on author's last two years of related research
Advanced courses and seminars can be taught out of this brief book.
All necessary background and motivations are given per chapter. A
related list of references is given also per chapter. The exposed
results are expected to find applications in many areas of computer
science and applied mathematics, such as neural networks,
intelligent systems, complexity theory, learning theory, vision and
approximation theory, etc. As such this monograph is suitable for
researchers, graduate students, and seminars of the above subjects,
also for all science libraries.