neural network applications

"The computer programmer is a creator of universes for which he alone is responsible. Universes of virtually unlimited complexity can be created in the form of computer programs."
- Joseph Weizenbaum

Neural Networks are programs designed to simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons which are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data. There are many different types of Neural Networks, each of which has different strengths particular to their applications. The abilities of different networks can be related to their structure, dynamics and learning methods.

What can you do with a Neural Network?

In principle, Neural Networks can compute any computable function, thus do everything a normal digital computer can do.

In practice, Neural Networks are especially useful for classification and function approximation/mapping problems which are tolerant of some imprecision, which have lots of training data available, but to which hard and fast rules (such as those that might be used in an expert system) cannot easily be applied. Almost any mapping between vector spaces can be approximated to arbitrary precision by feedforward Neural Networks (which are the type most often used in practical applications) if you have enough data and enough computing resources.

Neural Networks are, at least today, difficult to apply successfully to problems that concern manipulation of symbols and memory. And there are no methods for training Neural Networks that can magically create information that is not contained in the training data.


NN applications are almost limitless but fall into a few simple categories:


Among many applications of the feed-forward ANNs, the classification or prediction scenario is perhaps the most interesting for data mining. In this mode, the network is trained to classify certain patterns into certain groups, and then is used to classify novel patterns which were never presented to the net before. Medical diagnosis, signature verification, voice recognition, image recognition, property valuation, is only a few examples of this category.

Interactive demonstrations

Being familiar with Neural Networks will allow you to understand these demonstrations. Please read the section on About Neural Networks before running these demonstrations.
The new and improved Hopfield Net
Hopfield Network: Recognising Numbers
A working Perceptron Network

[These Java demonstrations brought to you by the Neural Transmitters behind The Mind and Machine Module.]


Neural Networks is and can be used to predict all sorts of things. Applications include future sales, production requirements, market performance, economic indicators, energy requirements, medical outcomes, chemical reaction products, and weather.


Artificial neural networks are flexible multivariate models that can be applied to predictive modeling and pattern recognition problems. Process control, systems control, chemical structures, dynamic systems, signal compression, plastics molding, welding control and robot control, to name a few, would fall under this category.


Past and Present

The development of true Neural Networks is a fairly recent event, which has been met with success. Two of the different systems (among the many) that have been developed are: the basic feedforward Network and the Hopfield Net.

The Future

The future of Neural Networks is wide open, and may lead to many answers and/or questions. Is it possible to create a conscious machine? What rights do these computers have? How does the human mind work? What does it mean to be human?