An Artificial Neural Network (ANN) is a part of a computer system that mimics the behavior of human neurons in the brain. A neural network is a machine learning algorithm based on the model of human neurons.
The neuron is the basic working unit of your brain, a specialized cell designed to transmit information to other nerve cells, muscle fibers, or gland cells. They are responsible for sending and receiving signals in humans through changes in voltage that take place when neurons fire or stop firing.
In the Artificial Neural Network (ANN) used in computing technology, each neuron processes various forms of input information and creates a single output unit, depending on its activation function—weight and type connection with other neurons. ANNs use a technique called backward propagation to recognize patterns in its process and to refine weight internal connections to reduce errors.
There are several types of ANNs with different structures that serve different purposes. New network structures are developed every day, so a comprehensive list of types is impossible.
The most prominent are:
· Feedforward Neural Networks Feedforward networks are the most common type of network. It contains three segments (input, hidden layers in between) through which information passes linearly.
· Recurrent Neural Networks Recurrent neural networks are similar to feedforward networks, but instead of simply passing the output through a series of hidden layers, they pass it back into themselves. They’re frequently used for autocompletion functions.
· Radial Basis Network A radial basis network uses the distance of multiple points relative to the center as an activation function. They’re often used in power systems to prevent blackouts and restore power as quickly as possible.
· Convolutional Neural Networks This is a type of neural network that separates data into various layers so it can be processed sequentially. There are no connections between all the layers, only links withneighboring ones to prevent information from being leaked. This technique works well for image recognition and natural language processing applications.
There are several valuable advantages of using ANNs. Some are discussed below.
Entire Network Storage
This advantage is like other traditional programming, where information is stored on the network and not on a database. If any of that information disappears from one place, then the whole system still works since everything else remains intact.
Work With Insufficient knowledge
The importance of that missing information determines the lack of performance. Data may produce an incomplete or insufficient output after ANN training, so it is important to know what types and how much data are required for successful results.
Good Fault Tolerance
Artificial neural networks are created so that they can tolerate faults and generate better outputs, even if one or more cells have been corrupted.
To teach an artificial neural network, the desired output of each example must be outlined. The more instances that are inputted to a network, its ability for learning will increase exponentially.
Though a network’s quality will inevitably degrade over time, it won’t immediately corrode the entire system.
Ability To Train Machine
ANN is a machine learning algorithm. ANN can learn from events and make decisions through commenting on similar events.
The Ability Of Parallel Processing
These networks can perform more than one function at a time because they have numerical strength.