Feedforward and Feedback Neural Networks: What’s the difference?

Neural networks are a new form of Artificial Intelligence. It is used to replicate the proper functioning of a human brain that is also capable of predicting non-linear time series. Hence, it can be said that neural networks are developed to make accurate forecasts. Neural networks search for patterns, learn them and classify them so that the computer brain can make predictions.

Feedforward Neural Networks

Feedforward neural networks follow only one direction and one path, that is, the result will always flow from input to output. In such a network, loops are not present and the output layer acts distinctively from the other layers. These neural networks are predominantly used in pattern recognition. The organizations that use feedforward neural networks are often given names like bottoms up, top-down, etc.

All the outputs are weighed and then transferred respectively to the next layer of neurons, commonly known as the hidden layer. The input to this layer can be the output for the next layer and this process goes on. Generally, one hidden layer is used in such a network.

Feedback Neural Network

Feedback neural networks do not follow any single path of transferring signals. These kinds of networks can have signals travelling from both directions, that is, from input to output as well as from output to input. Feedback neural networks are a bit complex when compared to feedforward neural networks as signals are constantly travelling from both sides.?

These networks also possess a sense of dynamism. Feedback neural networks aim to attend a state of equilibrium and these networks achieve it by constantly changing themselves and by comparing the signals and units. The state of equilibrium is maintained until there is a change in input. When the input changes, the network tries to achieve a new point of equilibrium.

Various feedback neural network researchers have defined these networks as recurrent or interactive networks. These are generally associated with organizations that have an individual layer. The prime benefit that the feedback network model offers is that the deep neural network algorithm specifies an actual feedback system and a secondary feedback system acts as a backup to generate the result.

A comparison

In a feedforward network system, an external load always exists to receive the signals that are passed on. On the other hand, in the case of a feedback network system, the output depends upon the signal that is generated by the secondary feedback system. Feedforward network systems need the 'measure of disturbance' whereas it is not required in the feedback network system. The feedforward neural network has an open loop but the feedback neural network has a closed loop.?Input is more essential in a feedforward network system whereas the output is the most essential part of a feedback network system.

In the feedforward network system, the adjustment of the variables takes place on the basis of knowledge. On the other hand in a feedback network system, the variables are adjusted based on the errors.

Final thoughts

Just like Artificial intelligence and Machine Learning, neural networks have also grown to be a part of this rapidly growing world. Neural networks are nothing but a minor part of the term, 'Artificial Intelligence. Such networks have provided tremendous assistance when wanting to make forecasts. You can also go through?a tour of attention-based architectures?to know how architectures in building networks work.