Feed Forward Neural network

A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do not form a cycle. The feedforward neural network was the first and simplest type of Artificial Neural Network. In this network information moves in only one direction i.e., it moves from input nodes to hidden nodes and from hidden nodes to output node. These networks having no cycles or loops. The data may be passed into multiple hidden layers but it moves always in forward direction and never moves backward. The simplest kind of Neural network is Single layer Perceptron Network, which consist of single layer output layer.

How does a Feed Forward Neural Network Work?

A Feed Forward Neural network is commonly seen in its simplest form as a single layer perceptron. In this model, a series of feed forward neural networks with the intension of running them independently from each other but with a mild intermediary for moderation. Like the Human brain process relies on many induvial neurons on order to handle and process networks perform their tasks independently, the results can be combined at the end to produce a synthesized and cohesive output.

Applications of Feed Forward Neural Network:

• o Data Compression
• o Pattern Recognition
• o Computer Vision
• o Sonar Target Recognition
• o Speech Recognition
• o Handwritten Characters Recognition