Neural networks are computational models inspired by the human brain that can learn and predict outcomes. They have practical uses in financial calculations, weather forecasting, medical diagnosis, and credit rating determination.
Neural networks are complex computational models that are often used for pattern recognition. Because neural networks are modeled after biological brain functions, they are able to ‘learn’ and predict outcomes. There are many practical uses of neural networks for forecasting, including financial calculations, weather forecasting, and medical diagnosis.
Artificial neural networks for prediction are inspired by the human brain. In a biological brain, many small processing units called “neurons” are connected in a large network. Each individual area of processing is relatively simple, but the entire network is capable of solving complex problems when every neuron works together. The connections between each tiny neuron can be reconfigured into new network patterns. This allows the brain to reorganize itself and “learn” new concepts.
Like a human brain, an artificial neural network contains many tiny processors and connections, which can be reconfigured. The concept of using artificial neurons was first described by scientists Walter Pitts and Warren McCulloch in 1943. This scientific work was soon expanded and publicized by the famous artificial intelligence pioneer Alan Turing, who wrote about artificial neural networks in a 1948 publication entitled “Intelligent Machinery .”
Financial computing is one of the most common uses of neural networks for forecasting. In essence, a neural network is used as a mathematical “filter” to predict an outcome based on available financial data. This feature is often used in stock market forecasting software. In this application, a computer processes previous market trends. Once a model is established, the neural network calculates whether a stock will rise or fall in the future.
Neural networks can also be used to determine an individual’s or a company’s credit rating. As with stock prediction, pattern recognition is key. A network can consider thousands of past borrowers and analyze their financial history. By finding past trends, forecasting neural networks can estimate which new applicants are at risk of losing credit. These individuals receive a prediction-based high-risk credit rating.
Similarly, neural networks can be used for weather forecasting. Many different environmental factors such as temperature and wind currents can be fed into the grid. Using a forecast model based on previous climate models, the neural network can determine the likely outcome of current weather conditions.
Using neural networks for prediction can also help solve some medical problems. The human body is very complex and dozens or even hundreds of factors can combine to cause a medical condition. Neural networks are sometimes able to infer the origin of a symptom. In this application, an artificial network can find trends and patterns from previous patient records and predict the most likely cause of a disease.
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