Abstract
Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN.
Highlights
During industrialization, remains a timeless manufacturing goal: To produce high quality products at minimum cost
Udo (1991) solved warehousing problems using Artificial Neural Networking (ANN), and the results indicated that the ANN algorithms performed efficiently with the reduction in 66% computation time and average memory storage [38]
This paper provides a comprehensive overview of the applications of ANN in various fields of life
Summary
Remains a timeless manufacturing goal: To produce high quality products at minimum cost. Due to vagueness and sheer extent of items to be considered in calculation approaches cannot rationally resolve the problem; ample development needs to be done in both the neural network complexity and computing capacity prior to real-intelligent program formation [3] These expert systems are found to be least efficient and effective in modern manufacturing systems due to complex, varying, and highly productive manufacturing environment. In this modern era, there is a strong need of AI category that must capable to respond quick alternations within automated productive environment; it must be able to learn and absorb quick changes, identify the trends, and forecast human being thoughts with minor human interferences. Introduction, working principle, various ANN classes, and structure of ANN
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