UNDERSTANDING THE THEORY, MODELS, AND APPLICATIONS OF ARTIFICIAL NEURAL NETWORK
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Abstract
The primary objective of the Artificial Neural Network (ANN) is to build useful ‘computers' for serious challenges and to reconstruct intelligent
data methodological approaches like pattern recognition, classification, and generalization through using simple, distributed, and robust
processing units known as artificial neurons. ANNs are parallel implementations of non-linear static-dynamic systems that are fine-grained. The
considerable degree of interconnectivity that provides neurons their great computing capacity through their vast parallel-distributed structure
gives ANNs their intelligence and ability to tackle difficult issues. The recent spike in demand in ANN is primarily due to the fact that ANN
algorithms and architectures may be deployed in real-time applications using VLSI technology. The scope of ANN applications has exploded in
recent years, fueled by both theoretical and practical accomplishments across a wide range of fields. The theory, models, and applications of
artificial neural networks are briefly discussed
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