Types of Neural Networks And Their Classification

Introduction to Neural Networks

  1. Feed-forward Neural Network
    This is the simplest model of a Neural network. Feed-forward neural networks are fast while used; however, from a training perspective, it is a little slow and takes time. Most of the vision and speech recognition applications use some form of feed-forward type of neural network.
    The feed-forward network is non-linear. The primary reason for these networks to be called feed-forward is that the flow of data takes place in the forward direction more so the data travels in a unidirectional way viz. input to output. Different functions can be arranged to depict these networks. Each model can be depicted as a graph where the functional groups are described. An example could be, three functions f(1), input layer one, f(2) is layer two, and f(3) is the output layer. So the information is passed from the input layer to the next layer where the computation takes place, which in turn gets passed to the output layer.
  2. Radial Basis Functions (RBF) Neural Network
    In this type of neural network, the data is grouped based on its distance from a center point. In situations where there is no training data, the data is grouped, and a center point is created. This network is designed to look for data points that are similar to each other and then group the data. An example application of this type of neural network is Power Restoration system.
    To explain further for better understanding, a Radial Basis Function (RBF) neural network has three layers — an input layer, a hidden layer, and an output layer. The hidden layer is non-linear, and the output layer is linear. Applications of RBF networks are image processing, speech recognition, and medical diagnosis.

RBF Networks — The three layers — Details:

Input Layer

Hidden Layer

Summation Layer

  • In PNN/GRNN networks, each point in the training file has one neuron. In the case of RBF networks, there are variable numbers of neurons that are generally lesser than the number of training points.
  • In small to medium-sized training sets, PNN or GRNN networks generally are more accurate than RBF networks. The downside is that PNN or GRNN networks are not practically suitable for large training sets.

Kohonen Self-organizing Neural Network

Recurrent Neural Network

Long Short Term Memory (LSTM)

Gated Recurrent Neural Network (GTU)

GRUs Vs LSTMs

  • RNN is intelligent enough to remember every piece of information across the network and is very useful in time series prediction. This is the primary reason that it is used in such kinds of applications as it can remember previous inputs as well.
  • Inputs of any length can be processed in this model.
  • Exploding and gradient vanishing is common in this model.\
  • Training an RNN is quite a challenging task.
  • It cannot process very long sequences if using ‘tanh’ or ‘relu’ as an activation function.

Convolution Neural Network

  • CNNs eliminate the need for manual or human intervention feature extraction efforts.
  • CNNs deliver the highest quality results in recognition results.
  • CNNs enable building on pre-existing networks, thus retraining for new recognition tasks is made possible.
  • Micromanagement of input features is possible. In this sense, the input features are handled as batches. This permits the network to remember an image in several parts.

Modular Neural Network (MNN)

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Nitesh S

Nitesh S

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