Artificial neural networks are related to the biological neural network of the human brain. The model consists of neurons that send and receive messages once they reach a certain threshold of capacity. The artificial system is made up of 3 layers; the input layer, the hidden layer, and the output layer. The input can come from many sources and its' data is filtered into the hidden layer. In the hidden layer, an algorithm trains the data to match it to a certain specified result. Within the hidden layer, interconnected nodes are added as the solution gets closer to the desired answer. Once the model finds an answer that is suitable it delivers the result to the output layer.
Neural network applications are often found in error-detection systems for heavy machinery in manufacturing. For example, take a piece of machinery that makes several cuts a day. Over time the blade will become dull. What neural networks aim to do in this scenario is cut down the time it takes to change out a blade in the process, because the longer the machine is down, the more cost a company faces. The machine is equipped with special sensors that detect symptoms of a worn blade, so that operators can quickly understand and anticpate a change before it occurs.
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