Foundations of Artificial Neural Networks

  • Andrzej Bielecki
Part of the Studies in Computational Intelligence book series (SCI, volume 770)


The rapid growth of computational power of computers is one of the basic qualities in the development of computer science. Therefore, informatics is applied to solving more and more complex problems and, what follows, the demand for bigger and more complex software occurs. It is not always possible, however, to use classical algorithmic methods to create such a type of software. There are two reasons for it. First of all, a good model of the relation between the input and output parameters often either does not exist at all or it cannot be created at the present level of scientific knowledge. It is worth of mentioning that the algorithmic approach requires the knowledge of the explicit form of the mapping between the aforementioned sets of parameters. Secondly, even if the model is given, the algorithmic approach can be impossible regarding its over-complexity. It can be both complexity of the task on the stage of the algorithm creating, and too slow working of the implemented system. The latter one is a critical parameter especially in the on-line systems. Therefore, the alternative approaches, in comparison with the classical algorithmic approach, are developed intensively. Artificial neural networks are included into this group of methods.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Automation, Computer Science and Biomedical EngineeringAGH University of Science and TechnologyCracowPoland

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