Also known as Neural Network, in information technology system an Artificial Neural Network (ANN) is a functionality of software and hardware patterned following the performance of neurons in the human brain.
Neural network theory follows the idea of certain key mechanisms of biological neurons, which can be extracted and used to simulations. In ANN, there are two essential things need to understand, first, that the parts of an ANN are an attempt to regenerate the computing possibility of the brain.
Secondly, no one ever claimed to imitate anything as complex as a human brain. Whereas, our brain is estimated to have around 100 billion neurons, however, a neural network contains only around 1,000 artificial neurons.
Most of the ANN system implement mechanism of ‘learning rule’ that re-module the weights of the connection as per input patterns i.e. presented with. In a sense, Neural Network system learn through examples likewise their biological counterparts.
Although neural network implemented different types of learning principles, we are concerning here with the data rule. Which is generally utilized by the most basic class of ANN known as “Backpropagation Neural Network (BPNN)”. BPPN is an abbreviation to the propagation of error.
Here, when a neural network presented essentially with a pattern it starts guessing randomly as to what it might be. It then looks how far the result was from the exact one and makes a proper adjustment to its connection weights.
Regularly, backpropagation functions a gradient descent into the space of solution’s vector toward a global minimum alongside to the steepest vector of the miscalculation surface. The global minimum is the theoretical result with the fewest error.
Since the nature of the fault space can’t be known a prior, ANN error detection often requires a huge number of individual runs to find out the best solution. Neural network also can be over-train, which defines that the network is trained preciously to communicate to single input that is similar to rote memorization.
Working of ANN
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ANN have many different properties, which make them smart alternative to traditional solution techniques. Two of the important alternatives to using neural network system are to build an algorithmic solution and to implement an expert system.
Algorithmic system arises when there is proper information about the data and the underlying theory. By knowing the date and the theoretical relationship within the data, we can have direct solutions from the error space. Ordinary von Neumann computers can be used to take out these relationships instantly and efficiently by a numerical algorithm.
Advance Systems, in a contrary, are used in the conditions when there is insufficient information and theoretical background to generate any kind of a suitable problem module. In these conditions, the smartness and the rationale of human experts are implemented by coding into an expert system.
An expert system is usually can perform in a good way in the absence of an exact problem module and complete data.
On the basis of above information, we can see the possible different applications of neural networks. Such as in airplanes, it can use as a basic autopilot, with the help of input units reading signals from the different cockpit system and output units re-modeling the plane’s control properly to keep it safely on course.
ANN has many applications related to security as well; suppose you’re running a bank with thousands of credit-card transactions functioning through your computer system every single minute. You require an instant automated system to identifying the transactions. Which might be fraudulent, it is something for that neural network is perfectly suitable.
In our regular life, we need to recognize patterns and use them to make decisions, so ANN can be helpful for thousands of different fields.
It can be helpful to forecast the weather, stock market, and to operate radar scanning system, which automatically finds the enemy ships or aircraft. It can even help doctors to diagnose serious diseases.
In our mobile and laptop, we are using voice recognition software, image recognition software and in email system emails are categorized itself, also while we text we have the auto-correct facility in our mobile and computer systems all of these are part of the neural network.
So in coming days, there is a large possibility of overtaking old technology by ANN in different fields, which can provide us a greater technological advancement in future.