Artificial intelligence and machine learning are popular, growing fields throughout the global economy. There is considerable interest in the machines used for these processes and the machine learning algorithm needed to make sense of massive quantities of data. However, a machine learning algorithm needs to be properly processed in order to work. That is where the artificial neural network can play an important role. Artificial neural networks serve as the substructure for a large percentage of machine learning that occurs on a regular basis. These networks help break up tasks, ease the transition from input to output, and allow a network to learn and grow as quickly as possible.
Artificial Neural Networks and the Brain
The human brain is clearly the baseline for many computer programs and artificial intelligence approaches. It is more efficient and elastic than any computer today. Examples of the artificial neural networks algorithm are focused on replicating the thought and reasoning patterns of the human brain. They replicate the activities and connections of neurons which work together to process information and relay output. The split nature of the network means that different task aspects can be performed by different nodes. This combination ensures that the neural network processes data as quickly and efficiently as possible.
Structure of Artificial Neural Network Algorithm
An artificial neural network is made up of a series of nodes. Nodes are connected in many ways like the neurons and axons in the human brain. These nodes are primed in a number of different ways. Some are limited to certain algorithms and tasks which they perform exclusively. In most cases, however, nodes are able to process a variety of algorithms. Nodes are able to absorb input and produce output. They are also connected to an artificial learning program. The program can change inputs as well as the weights for different nodes.
Node Output Evaluation
Weighting places more focus on different nodes or different functions of the same group of nodes. This network also has a function for displaying and evaluating the output. Evaluation is at the heart of the neural network and what makes it intelligent. Many types of computer systems run off of networks that have different nodes which perform different functions. However, weighting and artificial evaluation immensely increase the number of possible functions for the network.
Once it is established, the machine learning algorithm must be assigned and implemented. These networks need a basic type of function to be implemented by an operator. This function can be one of many different forms such as Apriori or k-means clustering. The artificial neural network then goes to work. It absorbs input and sends different tasks to different nodes. These nodes may work independently or they may frequently communicate with one another. The nodes then transfer an output to an end series of nodes.
Learning with Artificial Neural Network
An artificial neural network’s next steps depend on the type of learning being used by the network. In a supervised learning system, the output nodes are already programmed with example sets. Example data sets are the ideal that the network is trying to reach. After the initial data output, the network analyzes how close it was able to get to the example set.
The network then changes the weights applied to different nodes throughout the network in order to reach even closer to the original example set. These complex arrangements may occur thousands of times at least. The point is to make a network that can change and adapt to shifting conditions with a set number of nodes.
Unsupervised Learning and Neural Networks
Neural networks using unsupervised learning change in different ways. They still learn artificially and change weights depending on previous outcomes. However, there is not an example set to serve as a basis. Instead, the network shifts and changes depending on preconceived guidance and general circumstances. Its work can later be analyzed for how well it answered certain problems or figured out different patterns. The process is more free-flowing and less tied to a particular outcome. While these two approaches are somewhat different, the artificial neural networks algorithm used extensively in both unsupervised and supervised learning settings.
Artificial Neural Network Uses
There are multiple uses for an artificial neural networks algorithm. These networks are helpful for processing many types of data. One possible usage is to figure out patterns in an existing set of data. The neural network can analyze different strains of a data set using an existing machine learning algorithm or a new example. Those algorithms can result in regression lines or logistic relationships being detected. Some algorithms may be able to place the information being fed into a neural network into categories. More categories give a researcher a better understanding of the nature of his or her data.
Artificial Neural Network Prediction Algorithm
Another use of an artificial neural networks algorithm is tracking progress over time. With both supervised and unsupervised learning, an artificial neural network can be fine-tuned to make an accurate prediction or accurately weight and process data. Then, the network can be fed a steady stream of data with a steady number of changing example sets or guidelines. The artificial neural network can then change weighting in order to achieve the desired outcomes. As a result, the artificial neural network can become an accurate tool with minimal interference from a human operator.
As a relatively new phenomenon, the artificial neural networks algorithm still has room to improve and grow. They are being exposed to more algorithms and data sets on a daily basis. The architecture behind these structures is being improved with new and more powerful chips and computers. By using artificial neural networks, artificial intelligence has unlimited potential to transform nearly every sector of the modern economy.