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Fusion: Practice and Applications
Volume 6 , Issue 2, PP: 43-49 , 2021 | Cite this article as | XML |PDF


A Hybrid Approach for Neural Network in Pattern Storage

Authors Names :   Kumud Sachdeva   1 *     Shruti Aggarwal   2  

1  Affiliation :  Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India

    Email :  kumud.cse@cumail.in

2  Affiliation :  Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India

    Email :  drshruti.cse@gmail.com

Doi   :   https://doi.org/10.54216/FPA.060201

Abstract :

Your mind does not manufacture your mind. Your mind forms neural networks. Neural networks had been effectively carried out to numerous sample garage and type troubles in phrases in their mastering ability, excessive discrimination electricity, and exceptional generalization ability. The achievement of many mastering schemes isn't always assured, however, seeing that algorithms like backpropagation have many drawbacks like stepping into the nearby minima, for that reason imparting suboptimal solutions. In the case of classifying big sets and complicated patterns, the traditional neural networks are afflicted by numerous problems inclusive of the dedication of the shape and length of the network, the computational complexity, and so on. This paper introduces neural computing techniques especially radial foundation features network. Various upgrades and trends made in an artificial neural network for rushing up training, keeping off nighborhood minima, growing the generalization capacity and different capabilities are reviewed.

Keywords :

Neural Network; Artificial Neural Network; Artificial Intelligence; Hopfield Network; Radial Basis Function.

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Cite this Article as :
Kumud Sachdeva , Shruti Aggarwal, A Hybrid Approach for Neural Network in Pattern Storage, Fusion: Practice and Applications, Vol. 6 , No. 2 , (2021) : 43-49 (Doi   :  https://doi.org/10.54216/FPA.060201)