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Survey on Neural Networks, Algorithm, Types and its Application


     

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Neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory Neural network is initially trained or fed large amounts of data and rules about data relationships. Neural networks use several principles like gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are described in terms of knowledge layers, with general, more complex networks having deeper layers. Neural networks are used in forecasting applications and business classification applications due to their ability to “learn” from the data, their nonparametric nature and their ability to generalize. The comparative study on the algorithm is done in this paper.


Keywords

Artificial Neural Network, Neurons, Networks, Perceptron.
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  • Survey on Neural Networks, Algorithm, Types and its Application

Abstract Views: 256  |  PDF Views: 3

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Abstract


Neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory Neural network is initially trained or fed large amounts of data and rules about data relationships. Neural networks use several principles like gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are described in terms of knowledge layers, with general, more complex networks having deeper layers. Neural networks are used in forecasting applications and business classification applications due to their ability to “learn” from the data, their nonparametric nature and their ability to generalize. The comparative study on the algorithm is done in this paper.


Keywords


Artificial Neural Network, Neurons, Networks, Perceptron.



DOI: https://doi.org/10.36039/ciitaas%2F6%2F1%2F2014%2F106788.