The Artificial Neural Network (ANN) is a well-known and widely used method of estimating various values which appear in scientific and engineering research processes. One important application of ANNs is estimation. This method is used in a range of applications, such as medical research and engineering. ANNs have become increasingly popular in the past few decades as a result of their ability to accurately estimate values in a wide variety of situations and contexts.
In the field of engineering, the primary objective of ANN estimation is to accurately predict the output value of a given input (often known as the “response variable”). In general, when attempting to estimate a value, researchers must consider the impact of certain factors on the value in question. For example, when estimating the performance of a particular machine, one would take into account the design of the machine, the material used to construct it, its operating temperature, as well as other operational parameters. All these variables could be used to predict the output of the machine in question.
ANN estimation is used in a number of fields. In medical research, the primary purpose of the application of ANN estimation is to predict disease risks, assess the prognosis of disease, predict patient responses to treatments, and identify factors contributing to disease onset. For example, ANN estimation could be leveraged in the detection of cancer in individuals by comparing blood test results with medical history and environmental factors.
In engineering, ANN estimation is often applied to assess the possibility of failure of a particular machine or system. Specific parameters, such as temperature, pressure, force, and wear, can be evaluated and interpreted by an ANN in order to determine the probability of system failure. Additionally, ANN estimation could be used to predict the future performance of a machine or system by analyzing past performance. This can be helpful for engineers who may be tasked with making improvements to a machine or system, as they can compare the predicted performance and adjust their design accordingly.
ANN estimation is also used extensively in finance and economics. For instance, ANNs are often applied to develop stock trading models. Financial institutions use ANNs to predict the movements of stock prices, identify patterns in the stock market, and generate trading strategies. Meanwhile, economists use ANNs to forecast unemployment rates, GDP figures, inflation, and consumer spending.
Overall, ANN estimation is a valuable and powerful tool that can be applied in a variety of domains. This is because ANNs are capable of accurately predicting values despite being exposed to numerous changing factors that affect the outcome. They are also relatively simple to calibrate and program and can greatly reduce the amount of time spent manually researching the applicable factors and performing calculations. As such, ANN estimation has the potential to be a highly useful tool in many research and engineering fields.
