Artificial neural networks are computational models similar to human nervous systems. There are many sorts of artificial neural networks. The mathematical operations and parameters used to determine the output of these types of networks are implemented.

Here are some examples of neural networks:

  1. 1. Feedforward Neural Network – Artificial Neuron:

Data or input are transmitted only one way in this neural network, making it one of the simplest ANNs. The data is input by the input nodes and exits at the output nodes. It is possible for this neural network to have hidden layers. In simple terms, it uses a classification activation function to generate a wave with a front propagation and no backpropagation.

The following steps are usually taken in order to restore power:

The first priority is to restore power to the communities’ most important customers. By providing them with power first, we are enabling them to offer health and safety services to everyone. A few of the key customers include health care facilities, school boards, and municipalities’ critical infrastructure.

Consider the larger power lines and substations that serve greater numbers of clients

The priority should be given to repairs that will restore service to the largest number of customers the quickest.

  1. Kohonen Self-Organizing Neural Network:

As an input vector, Kohonen maps consist of neurons that contain vectors of arbitrary dimension. In order for the map to produce its own organization of the training data, it must be trained. There are two dimensions to it. The locations of the neurons in the training map remain the same, but the weights differ. Self-organization is a layered process in which each neuron has a weight and an input vector at the outset.

A neuron connected to the winning neuron moves towards the point like in the graphic below as the second phase unfolds. Euclidean distance calculates the distance between a point and its neurons; the neuron with the least distance wins. The clusters are formed through iteration, each neuron representing a different cluster. Kohonen Neural Network was founded on this principle.

Kohonen Neural Networks are used to recognize patterns in data. Applications include clustering data into different categories in medical analysis. With high accuracy, the Kohonen map was able to classify patients as having a tubular or glomerular disease.

  1. Recurrent Neural Network (RNN)

In a Recurrent Neural Network, the output of each layer is saved and fed back into the input in order to predict the outcome.

In the preceding equation, a feed-forward neural network is formed with a first layer that is formed by the sum of weights and features. Once this is computed, a recurrent neural network starts, which means that from one-time step to the next, each neuron remembers something from the last step.

When neurons are used to compute, they act as memory cells. Meanwhile, the neural network should work on the front propagation and remember what information it will require in the future. In this case, if the prediction is wrong, we will use the learning rate or error correction to make small changes during backpropagation so that it will gradually make the right prediction.

  1. Convolutional Neural Network:

In convolutional neural networks, the neurons have learnable weights and biases, similar to feed-forward neural networks. With its application in signal and image processing, it has been able to replace OpenCV in computer vision.

It is applied in techniques such as signal processing and image classification. Computer vision techniques are dominated by convolutional neural networks due to their accuracy in image classification. The technique of image analysis and recognition, where the agriculture and weather features are extracted from the open-source satellites like LSAT to predict the longer-term growth and yield of a particular land is being implemented.

  1. Modular Neural Network:

The output of a modular neural network is affected by a number of independent networks. As neural networks construct and perform sub-tasks, each has a unique set of inputs. In accomplishing the tasks, these networks do not interact or communicate with each other.

Modular networks simplify complex computations by breaking them into smaller components. By breaking them down, we can reduce the number of connections and reduce the interaction between these networks, which will result in faster computations. However, the amount of time it takes to process the data depends on the number of neurons involved.

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