Artificial Neural Networks also known as Neural Networks are computing systems that utilize the animal brain. It is based on the collection of connected units or nodes called artificial neurons which completely models the neurons in the biological system. Neural networks are trained by processing examples like input and result. An Artificial Neural Network is a component of Artificial Intelligence that is meant to simulate the functioning of the human brain. All the processing units of the Artificial Neural networks consist of inputs and outputs.
Artificial Neural networks are derived from biological neural networks that develop the structure of the human brain. These Networks also have neurons that are interconnected to one another in various layers of the network which are known as nodes. The neurons are arranged in a sequence of layers which are the input layer, hidden layer, and output layer.
Various advantages of ANN include: parallel processing capability, storing data on the entire network, capability to work with incomplete knowledge, having a memory distribution and, having fault tolerance. Its disadvantages include: assurance of proper network structure, unrecognized behavior of the network, hardware dependence, difficulty of showing the issue to the network, the duration of the network is completely unknown.
There are various types of ANN depending upon the human brain neuron and network functions. Its types include: Feedback ANN, Feed-forward ANN. in feedback ANN, the output returns into the network to accomplish better results. In feed-forward ANN, thorough assessment of the output, the intensity of the network can be noticed, and then output is decided. The advantage of this is that it figures out how to evaluate and recognize input patterns.