Artificial neural networks are widely used in machine learning. They operate just like our nervous system. There are different types of artificial neural networks. This is why it is extremely important to choose the right artificial neural network. However, there is no need to worry as this post shares information about the different types of artificial neural networks that are available today. These are mentioned as below.
- Modular Neural Networks
Modular neural networks consist of many independent networks which contribute collectively towards the end result. Each of the neural networks will be responsive for performing sub-tasks. It will involve unique inputs unlike most neural networks which do not use a set of unique inputs. These neural networks do not have any interaction or signal exchange to achieve the task. This helps break down the complexity of problems and allows for better problem solving. By doing this, the computational speed is also improved.
- Feed Forward Neural Network
Another interesting type of artificial neural network is the Feed Forward neural network. Information travels in a single direction in this network. It is considered to be the purest form of artificial neural networks. The neural network has hidden layers in which data enters from the input nodes and exists from the output nodes. As there is no back propagation, only front propagated waves are allowed. A great thing about Feed Forward neural networks is that they serve many purposes such as computer vision and speech recognition to name a few.
- Radial Basis Function (RBF) Neural Network
When it comes to an RBF neural network, there are two different layers for functioning. The first consists of an inner layer which is united with the RBF and the second is used for computing similar output. One of the uses of RBF is that it is visible in Power Restoration Systems. The goal is to restore power as quickly and reliably as possible during a blackout.
- Kohonen Self Organizing Neural Network
Vectors are inputted in this neural network to create a discrete map from arbitrary dimensions. This map can be trained using the training data of organizations. It would have one to two dimensions. The value influences the weight of the neurons. While the map is being trained, the location of the neurons will not change. The neuron closest to the point will be the winning neuron. As for the other neurons, they will move towards the point, whereas, the winning neuron will continue during the next phase. The main application of this network is the ability to recognize data patterns.
- Recurrent Neural Network (RNN)
Finally, an artificial neural network which utilizes feedback is the Recurrent Neural Network. It predicts the outcome of each layer. Each neuron would act like a memory cell during the computation process. It will retain information as it proceeds to the next step. Hence, the name Recurrent Neural Network is perfect. Recurrent Neural Networks offer many applications including text to speech conversion.
Conclusion
Artificial intelligence and machine learning use different types of neural networks. Each network serves different purposes.