Connection Enitity of Artificial Neural Network

Connection enitity of artificial neural network The models of artificial neural networks are specified by three basic components or entities. The first one is the model's synaptic interconnections. The second one is the training or learning rules adopted for updating and adjusting the connection weights. The third one is the activation functions. We will try to discuss each of these components one by one. The first one is the connections. In artificial neural networks, we have multiple numbers of neurons interconnected with one another in multiple layers, such as the input layer, hidden layer, and output layer, and so on. There exists basically five types of interconnections. The first one is a single-layer feedforward network. The second one is a multi-layer feedforward network. The third one is a single node with its own feedback. The fourth one is a single-layer recurrent network. The fifth one is a multi-layer recurrent network. This is how the single-layer feedforward network looks like. In this case, we have two layers: the first one is the input layer, and the second one is the output layer. The input layer has one or more neurons, and the output layer can have one or more neurons in this case. Each of the neurons present in the input layer are directly connected with the output layer neurons. That is, X1 is connected to Y1, Y2, and YM in this case. Similarly, X2 is connected to Y1, Y2, YM, and so on. This is also called a fully connected single-layer feedforward network. In this case, the input layer receives the inputs from the input, and then that input is forwarded to the output layer, and the output will be received at this particular layer. This is how the multi-layer feedforward network looks like. It has an input layer and an output layer, along with one or more hidden layers. In this case, the input layer has one or more input neurons, and the output layer has again one or more output neurons based on the problem we are solving. The hidden layer can have, again, one or more neurons. In this case, the input layer neurons are connected to the hidden layer neurons, and the hidden layer neurons are connected to the output layer neurons. Coming back to the third one, that is a single node with its own feedback. In this case, we have only one layer, that is the input layer, and the output of the input neuron is nothing but the output here. So, we want to validate whether this particular output is acceptable or not. If it is acceptable, it's okay. If it is not acceptable, we will send back the feedback, and then we will modify the computation part of this particular input neuron so that we will get a better output. In this case, the next one is, uh, the single-layer recurrent network. In this case, we have an input layer and an output layer. While modifying or updating these particular weights, rather than just considering the input as well as the previous weights, we consider the outputs as well. In this case, the output of the previous iteration and then the input will be considered to update these particular weights over here. So, that is what is called a single-layer recurrent network. Multi-layer recurrent network has a similar architecture, but we have, what is that called, multiple numbers of layers. In this case, along with the input and output layer, we have what is called the hidden layer also in this case. So, these are the different types of connections we can have in the artificial neural network.