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What Are the Different Types of Neural Applications?

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  • Written By: L.S. Ware
  • Edited By: Melissa Wiley
  • Last Modified Date: 03 September 2016
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Artificial neural networks have been developed based on human brain functions as a processing mechanism. The neural applications that have evolved from this technology are many and varied. They are used to improve performance in such areas as virtual reality and data mining, where existing technology tends to fall short. Neural applications are also utilized in industries from the financial sector to the medical industry.

A number of factors affect the applications for which an artificial neural network is best suited. The structure and dynamics of the network as well as how the network learns all play a role in defining the abilities of these applications. Four primary categories help define the tasks and potential types of applications for neural networks.

Function approximation and data processing are similar categories that cover applications such as time series analysis and data filtering, respectively. The classification category is one of the more robust, containing applications that perform pattern and sequence recognition. Finally, robotics is its own category. Robotic neural applications assist with everything from mobility to manipulation.

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A single network can serve many neural applications, as the programs are designed to be customizable and learn as a part of the programming. Industrial applications include tasks such as predicting lake or reservoir levels, assisting with air traffic control, and optimization of chemical processes. Businesses can employ neural networks to facilitate the hiring process by analyzing applicants and predicting their performance or customer database analysis to assist in determining marketing groups. Optical character recognition, referred to as OCR, is among the most well known neural applications.

The financial and medical industries both employ neural applications with regularity. Financial companies use various data mining and analysis methods as well as prediction systems. Automated trading systems often integrate neural applications. Medical diagnosis is another application. Research, medical or otherwise, also benefits tremendously with the inclusion of a neural network to recognize patterns, process vast amounts of data, and perform predictive simulations.

Neural applications are becoming gradually more prevalent, especially in the development of robotics and virtual reality. Various methods of machine vision and mobilization can be optimized or controlled. Machine learning plays a big role in both fields by enabling robots to learn their environment and by adjusting virtual reality environments almost instantaneously based on user interactions. As neural networks become easier to work with and more robust, neural applications will continue to evolve and assist innumerable fields with tasks that are otherwise unwieldy.

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