Pattern Recognition System using MLP Neural Networks
Sarvda Chauhan · IOSR Journal of Engineering · 2012
Pattern recognition can be used to recognize and/or locate specific objects in an image.The pattern recognition approaches are based on analysis of statistical parameters computed using image processing tools.The parameters may be compared with the standard pattern parameters to identify the pattern or a neural network may be trained using the statistical parameters to identify a given pattern.In the presented work, a neural network approach has been worked out in identifying a pattern.The neural approach applies biological concepts to machines to recognize patterns.A neural network is an information processing system.It consists of massive simple processing units with a high degree of interconnection between each unit.The processing units work cooperatively with each other and achieve massive parallel distributed processing.The design and function of neural networks simulate some functionality of biological brains and neural systems.The advantages of neural networks are their adaptive-learning, self-organization and fault-tolerance capabilities.For these outstanding capabilities, neural networks are used for pattern recognition applications.In the presented work, multi-layer perceptron (MLP) neural network is used for pattern recognition.Here, each neuron computes a so called "net input" from the outputs of previous neurons and from the weights of the connections.Typically, such a net input is a weighted sum, and a numerical value, called "bias", is added to the net input.In MLPs, a function called "activation function", is applied to the net input.In our experiments, we used a sigmoid function.