By George A. Anastassiou (auth.)
This short monograph is the 1st one to deal solely with the quantitative approximation by way of synthetic neural networks to the identity-unit operator. the following we examine with premiums the approximation homes of the "right" sigmoidal and hyperbolic tangent synthetic neural community optimistic linear operators. particularly we research the measure of approximation of those operators to the unit operator within the univariate and multivariate situations over bounded or unbounded domain names. this is often given through inequalities and with using modulus of continuity of the concerned functionality or its larger order by-product. We study the true and intricate cases.
For the benefit of the reader, the chapters of this e-book are written in a self-contained style.
This treatise is determined by author's final years of similar learn work.
Advanced classes and seminars might be taught out of this short booklet. All helpful heritage and motivations are given in line with bankruptcy. A similar record of references is given additionally in line with bankruptcy. The uncovered effects are anticipated to discover functions in lots of components of machine technology and utilized arithmetic, resembling neural networks, clever structures, complexity conception, studying conception, imaginative and prescient and approximation concept, and so forth. As such this monograph is acceptable for researchers, graduate scholars, and seminars of the above topics, additionally for all technology libraries.
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