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A STUDY ON ARTIFICIAL NEURAL NETWORKS

Profile image of Furqan Y A Q U B khanFurqan Y A Q U B khan

2018, A STUDY

Abstract

First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.

Key takeaways

  • Some computers are using neurons to control activities termed as neural artificial intelligence.
  • Working of basic unit of ANN(NEURON).
  • We can artificially mimic it through semantic network processing system (SNePs).
  • With the rise of digital economy ANN has changed because it need large amount of data to be stored processed and developed which can be only through ANN as I presume it will become a wide trend in future.
  • Croudsourcing is important aspect of AI as computers alone can't solve the problems but with human intelligence it can.

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