Hypothetical Pattern Recognition Design Using Multi-Layer Perceptron Neural Network For Supervised Learning
International Journal of Scientific & Technology Research, ISSN. 2277-8616, Volume 4, Issue 12, December 2015, pp. 97-102.
Humans are capable to identifying diverse shapes in different patterns in the real world an effortless fashion due to their intelligence is growing since being born with facing several learning process. The same way we can prepare a machine using human-like brain (called, Artificial Neural Network) that can recognize different patterns from the real-world object. Although various techniques is exists to the implementation pattern recognition but recently the artificial neural network approaches have been giving a significant attention. Because the approached of the artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research, now a day‟s pattern
recognition for machine learning using artificial neural networks got a significant achievement. For this reason many real-world problems can be solved by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer
Perceptron neural network(in the algorithm of artificial intelligence) as the best possible way of utilizing available resources to make a decision that can be a human-like performance.