4 benefits of using artificial neural nets

Artificial neural nets have a remarkable capability of deriving meaning from complicated or imprecise data. This ability can be used for extracting patterns and detecting trends that are complicated for computer systems to understand.

An artificial neural net is an information processing paradigm whose working is similar to the working of biological nervous systems. The key element of an artificial neural network is the novel structure of its information processing system. This structure consists of a large number of highly interconnected processing computing elements that work in unison to solve specific problems. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.

Four Benefits of Artificial Neural Nets

Organic Learning

Neural networks can learn organically. This means an artificial neural network’s outputs aren't limited entirely by inputs and results given to them initially by an expert system. Artificial neural networks have the ability to generalize their inputs. This ability is valuable for robotics and pattern recognition systems.

Nonlinear Data Processing

Nonlinear systems have the capability of finding shortcuts to reach computationally expensive solutions. These systems can also infer connections between data points, rather than waiting for records in a data source to be explicitly linked. This nonlinear short-cut mechanism is fed into artificial neural networking, which makes it valuable in commercial big-data analysis.

Fault Tolerance

Artificial neural networks have the potential for high fault tolerance. When these networks are scaled across multiple machines and multiple servers, they are able to route around missing data or servers and nodes that can't communicate.

Self-Repair

Artificial neural networks can do more than routing around parts of the network that no longer operate. If they are asked for finding out specific data that is no longer communicating, these artificial neural networks can regenerate large amounts of data by inference and help in determining the node that is not working. This trait is useful for networks that require informing their users about the current state of the network and effectively results in a self-debugging and diagnosing network.

Scientists are now trying to understand capacities, assumptions, and applicability of various approaches that can significantly improve the performance of artificial neural networking systems.