What is the use of neural networks for prediction?

neural networks are complex computational models that are often used to recognize patterns. Because neural networks are modeled on biological brain functions, they are able to “learn” and predict results. There are many practical use of neural networks, including financial calculation, weather forecast and medical diagnosis.

Artificial neural networks for prediction are inspired by the human brain. In the biological brain, many small processing units called "neurons" are connected to a large network. Each individual processing area is relatively simple, but the whole network is able to solve complex problems when every neuron cooperates. The connection between each small neuron can be reconfigured into new network patterns. This allows the brain to reorganize and "learn" new concepts.

Like the human brain, the artificial neural network contains many small processors and connections that can be reconfigured. The concept of using Artificial Neurons were first describedNi scientists Walter Pitts and Warren McCuloch in 1943. This scientific work was soon expanded and promoted by the famous pioneer of artificial intelligence Alan Turing, who wrote about artificial neuron networks in 1948 called "Intelligent Machines".

Financial calculation is one of the most common uses of neural networks for prediction. The neuron network is basically used as a mathematical "filter" to predict the result based on available financial data. This feature is often used in stock market prediction software. In this application, the computer processes previous trends on the market. Once the formula is set, the neuron network calculates whether the shares will increase or fall in the future.

neural networks can also be used to determine an individual or company credit evaluation. As with shares prediction, the pattern is recognized. The network may consider thousands of past recipients of the loan and analyze theirfinancial history. By finding past trends, neuron networks can estimate which new applicants are likely to fail on their credit. These individuals receive high -risk rating based on prediction.

Similarly, neural networks can be used to predict the weather. Many different environmental factors such as temperatures and wind currents can be brought to the network. Using the prognosis model, which is based on previous climate samples, the neuron network may determine the probable result of current weather conditions.

Using neural networks for prediction can also help solve certain health problems. The human body is very complex and tens or even hundreds of factors can connect to cause health. Neuron networks are sometimes able to derive the source of the symptom. In this application, the artificial network can find trends and patterns from the PREVIOUS patient records and predict the most likely cause of the disease.