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Artificial neural networks mimic the human brain and are used in various industries, including finance and medicine. They can perform tasks such as data processing, pattern recognition, and robotics. As they become more advanced, they will continue to assist in numerous fields.
Artificial neural networks were developed based on the functions of the human brain as a processing mechanism. The neural applications that have evolved from this technology are many and varied. They are used to improve performance in areas such as virtual reality and data mining where existing technology tends to fall short. Neural applications are also used in industries from the financial sector to the medical industry.
Numerous factors influence which applications an artificial neural network is best suited for. The structure and dynamics of the network, as well as how the network learns, play a role in defining the capabilities of these applications. Four main categories help define the tasks and potential types of applications for neural networks.
Function approximation and data processing are similar categories covering applications such as time series analysis and data filtering, respectively. The classification category is one of the most robust and contains applications that perform pattern and sequence recognition. Finally, robotics is in a category of its own. Robotic neural applications help with everything from mobility to manipulation.
A single network can serve many neural applications, as programs are designed to be customizable and learn as part of programming. Industrial applications include such tasks as predicting lake or reservoir levels, assisting air traffic control, and optimizing chemical processes. Companies can use neural networks to facilitate the hiring process by analyzing candidates and predicting their performance or customer database analysis to help determine marketing groups. Optical character recognition, called OCR, is among the best-known neural applications.
The financial and medical industries both use neural applications on a regular basis. Financial firms use various methods of data mining and analysis, as well as forecasting systems. Automated trading systems often integrate neural applications. Medical diagnosis is another application. Research, medical or otherwise, also benefits greatly from the inclusion of a neural network to recognize patterns, process large amounts of data, and run predictive simulations.
Neural applications are gradually becoming more widespread, especially in the development of robotics and virtual reality. Various methods of computer vision and mobilization can be optimized or controlled. Machine learning plays an important role in both fields by enabling robots to learn their environment and adapting virtual reality environments almost instantaneously based on user interactions. As neural networks become easier to use and more robust, neural applications will continue to evolve and assist countless fields with otherwise cumbersome tasks.
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