Random Neural Network Model for Supervised Learning Problems

Title:

Random Neural Network Model for Supervised Learning Problems

Creator:

Basterrech , S. and Rubino , G.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:240ac8a1-02ed-4d35-9dea-1d6a1070f76b
uuid:240ac8a1-02ed-4d35-9dea-1d6a1070f76b
doi:10.14311/NNW.2015.25.024

Subject:

neural networks, random neural networks, supervised learning, pattern recognition, and G-networks

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Random Neural Networks (RNNs) area classof Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised learning problems. We give a general description of these models using almost indistinctly the terminology of Queuing Theory and the neural one. We present the standard learning procedures usedby RNNs, adapted from similar well-established improvements in the standard NN field. We describe in particular a set of learning algorithms covering techniques based on the use of first order and, then, of second order derivatives. We also discuss some issues related to these objects and present new perspectives about their use in supervised learning problems. The tutorial describes their most relevant applications, and also provides a large bibliography.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:5

Harvested from:

CDK

Metadata only:

false