| Description |
1 online resource (lv, 848 pages) : illustrations (some color) |
| Series |
Gabler theses, 2731-3239 |
|
Gabler theses, 2731-3239
|
| Contents |
Introduction -- Problem Analysis and Problem Statement -- Objectives and Methodology -- Design -- Implementation -- Demonstration -- Evaluation -- Concluding Remark |
| Summary |
The business problem of having inefficient processes, imprecise process analyses and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS) and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes. About the author Dr.-Ing. Marcus Grum conducts research on neural networks and knowledge processing. The explainable and ethically justifiable integration of artificial intelligence into economic contexts is a major challenge and the subject of his research. He has worked on numerous research and customer projects in the areas of knowledge management, business process management, and artificial intelligence. He graduated from the studies of computer science as well as economics at the University of Potsdam, the Technical University of Berlin and the Humboldt University of Berlin |
| Bibliography |
Includes bibliographical references |
| Notes |
Abstract in English and German |
|
Online resource; title from PDF title page (SpringerLink, viewed March 4, 2022) |
| Subject |
Neural networks (Computer science) -- Mathematical models
|
|
Machine learning.
|
|
Machine learning
|
|
Neural networks (Computer science) -- Mathematical models
|
| Form |
Electronic book
|
| ISBN |
9783658359997 |
|
3658359994 |
|
