8.a) Analyze different types of Artificial Neural Networks with a diagram.
Answer:
Types of Artificial Neural Networks:
ANNs consist of multiple neurons arranged in layers. There are different types Of ANNs that differ by the network structure, activation function involved and the learning rules used. In an ANN, there are three layers called input layer, hidden layer and output layer. Any general ANN would consist of one input layer, one output layer and zero or more hidden layers.
- Feed Forward Neural Network
- Fully Connected Neural Network
- Multi-Layer Perceptron (MLP)
- Feedback Neural Network
1. Feed Forward Neural Network
This is the simplest neural network that consists of neurons which are arranged in layers and the information is propagated only in the forward direction. This model may or may not contain a hidden layer and there is no back propagation. Based on the number of hidden layers they are further classified into single-layered and multi-layered feed forward networks. These ANNs are simple to design and easy to maintain. They are fast but cannot be used for complex learning. They are used for simple classification and simple image processing, etc. The model of a Feed Forward Neural Network is shown in Figure 10.7.
2. Fully Connected Neural Network
Fully connected neural networks are the ones in which all the neurons in a layer are connected to all other neurons in the next layer. The model of a fully connected neural network is shown in Figure 10.8.
3. Multi-Layer Perceptron (MLP)
This ANN consists of multiple layers with one input layer, one output layer and one or more hidden layers. Every neuron in a layer is connected to all neurons in the next layer and thus they are fully connected. The information flows in both the directions. In the forward direction, the inputs are multiplied by weights of neurons and forwarded to the activation function of the neuron and output is passed to the next layer. If the output is incorrect, then in the backward direction, error is back propagated to adjust the weights and biases to get correct output. Thus, the network leams with the training data. This type of ANN is used in deep learning for complex classification, speech recognition, medical diagnosis, forecasting, etc. They are comparatively complex and slow. The model of an MLP is shown in Figure 10.9.
4. Feedback Neural Network
Feedback neural networks have feedback connections between neurons that allow information flow in both directions in the network. The output signals can be sent back to the neurons in the same layer or to the neurons in the preceding layers. Hence, this network is more dynamic during training. The model of a feedback neural network is shown in Figure 10.10.
