About Artificial Neural Networks

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Artificial Neural Networks

An artificial neural network is a networking system which follows the pathway of the biological neural network, that functions as an animal brain. To make animal understand or do something there is need of training them by showing the activity to be performed likewise, the system needs to learn how to do a particular work with the help of examples and rules given to them before operating like differentiating between cat and dog can be done by making system learn characters of dog.

More about Artificial Neural Networks

An artificial neural network system is formed by the interconnection of nodes referred to as artificial neurones, constituting structure is similar to that of animal brains. The interconnection helps nodes/neurons to send signals to each other which is then processed in passed to subsequent nodes. Output result given by nodes is calculated with the help of the non-linear function of the total sum of the input signal. The strength of a connection varies with weight. connections are also referred to as edges. While neurons and edges together are known as weight. Similar to convolutional neural network artificial neural network to has concept of threshold signal. Neurons transmit signals only when the aggregate total is above threshold value neuron in an artificial neural network are aggregated together into layers and each layer executes a unique series of functioning to the input. In this way, the signal passes through the first to the last layer that is from input to output this process of transformation takes place multiple times in accordance with different layers. This is the mechanism of ANN working.

With passing time artificial neural networks have transformed and broadened their scope. The most basic type has one or more static parts consisting of units, weights, layers and topology. Dynamic type permits to evolve with the help of learning. It is complex but ensures reduced learning time and better results. There is a certain classification based on supervised and unsupervised, former one requires operating while the latter operates on its own. There are other types available too, which run only on hardware and some which run only on software. It was built with an objective to perform operations in a way similar to the human brain's, but it deviated from its vision and now it serves tasks in a specific way. Like, medical diagnosis, automated game playing, voice recognition, etc.

Some of the applications and uses of artificial neural networks are as follows:

They are mostly used in the field of process control and management, system identification like vehicle control and path projection, image recognition, signal identification and mining of data.

Artificial neural networks have found there uses in medical diagnosis specifically in fields like lung and rectal Cancer determination.

Artificial neural networks are used to differentiate between unethical and vice activities performed on the web, that is cybersecurity.

Artificial neural networks are used to analyse and predict the structure and foundational stability of buildings when faced with disasters like earthquake.

Research in the field of brain and neurology is under process in coordination with artificial neural networks. It particularly helps in the study of the behaviour of neurons.

If selections involving model and algorithm, costing is accurate a robust artificial neural network can be obtained easily.

An artificial neural network is a new model with great potential, they are used in a diverse range of fields. With the improvement in its features and reduction in error this artificial neural network system will be easily effective software in future. High probability of ANN becoming a system was in, it won't need human assistance to correct the mistakes and guidance, meanwhile, it will be fully automated thus ensuring human work becomes less.