Artificial Neural Networks, which are also known as ANN has received a lot of attention in the recent past. The increasing awareness for artificial intelligence has contributed a lot towards their popularity. The Artificial Neural Networks can simply be defined as biologically inspired networks. In other words, these networks work similar to the human brain. As you already know, the human brain consists of billions of special cells named as neurons. It has been estimated that a typical human brain consists of about 150 billion neurons. These neurons are inter-connected with each other to form a network. As a result, the human brain can be considered as a collection of neural networks.
The Concept Of Neural Computing
The application of Artificial Neural Networks is known as neural computing. Neural computing uses a limited amount of concepts that are borrowed from biological neural systems. The main objective of Artificial Neural Networks is to simulate massively parallel processes that are associated with interconnected processing elements in network architecture. An artificial neuron would be the building block of Artificial Neural Networks. A neuron would receive inputs from the other neurons that are connected to it. On the other hand, the output of a neuron will be passed to several other connected neurons. The same mechanism takes place within the human brain as well. Likewise, the artificial signals processed by the neurons can be changed. The way how Artificial Neural Networks process information entirely depends on its structure. The algorithm that is being used to process information can also create an impact on it.
Artificial Neural Networks can be applied to a variety of scenarios. The benefits that are associated with these applications have contributed a lot towards their popularity. The most popular areas where ANNS are being applied include learning, pattern recognition and interpretation of noise and incomplete inputs.
Humans follow a different approach when solving problems when compared to computers. The objective of Artificial Neural Networks is to make computers think and solve issues like human beings. This can help computers to solve complex problems, which cannot be solved with a rule based approach. In other words, Artificial Neural Networks are being used to solve complex problems that cannot be simulated using analytical or logical techniques. They have the potential to solve issues, which cannot be solved even with expert systems. Pattern recognition is a perfect example to prove the above mentioned fact.
Artificial Neural Networks are in a position to analyze large amounts of data in an effective manner. After the analysis, it can establish characteristics and patterns, where rules or logic is not known. Loan applications are a perfect example for such a situation. After going through a large number of historical cases, the questionnaire of the applicant is being accepted or rejected. Only Artificial Neural Networks have the ability to automate this process of approving loans. If you need more about artificial intelligence, you can read more here
Artificial Neural Networks also have the ability to create profiles or patterns of applications that need to be denied or approved. Then a new application is matched against a pattern by the computer. The computer would gain the intelligence to classify whether it is a “yes” or “no”. Otherwise, it would go for the decision of humans. That’s the main reason why neural networks are being used for a variety of financial applications such as predicting exchange rates, predicting bankruptcy and determining when to purchase and sell stock.