From emergent
Jump to: navigation, search

Back to Comparison of Neural Network Simulators

Stuttgart Neural Network Simulator: Exploring connectionism and machine learning with SNNS.

On Google Scholar


Conventional algorithmic solution methods require the application of unambiguous definitions and procedures. This requirement makes them impractical or unsuitable for applications such as image or sound recognition where logical rules do not exist or are difficult to determine. These methods are also unsuitable when the input data may be incomplete or distorted. Neural networks provide an alternative to algorithmic methods. Their design and operation is loosely modeled after the networks of neurons connected by synapses found in the human brain and other biological systems. One can also find neural networks referred to as artificial neural networks or artificial neural systems. Another designation that is used is ``connectionism, since it deals with information processing carried out by interconnected networks of primitive computational cells. The purpose of this article is to introduce the reader to neural networks in general and to the use of the Stuttgart Neural Network Simulator (SNNS).

In order to understand the significance of the ability of a neural network to handle data which is less than perfect, we will preview at this time a simple character-recognition application and demonstrate it later. We will develop a neural network that can classify a 7x5 rectangular matrix representation of alphabetic characters.


	address = {Seattle, WA, USA},
	author = {Petron, Ed  },
	citeulike-article-id = {2716672},
	issn = {1075-3583},
	journal = {Linux J.},
	posted-at = {2008-04-25 05:20:47},
	priority = {2},
	publisher = {Specialized Systems Consultants, Inc.},
	title = {Stuttgart Neural Network Simulator: Exploring connectionism and machine learning with SNNS},
	url = {},
	year = {1999}