Introduction to Soft Computing

(Part 2)

• Architectures of Neural Network:

Artificial Neural Networks (ANNs) are powerful computational systems that mimic the way human brains process information. They consist of numerous interconnected units known as artificial neurons. These neurons work together to perform complex calculations and make predictions.

The single-layer neural network is the simplest architecture in Artificial Neural Networks (ANNs). It consists of just two layers: the input layer and the output layer.

In this architecture, the input layer comprises ‘m’ input neurons that connect directly to ‘n’ output neurons. Notably, the input layer does not perform any computations; it solely transmits input signals to the output layer. All processing occurs in the output layer.

Because only one layer is responsible for computations, this structure is referred to as a single-layer neural network. This simplicity makes it a fundamental building block for understanding more complex neural network architectures.

Also, the signals always flow from the input layer to the output layer. Hence, the network is known as FEED FORWARD.

The signal output from each output neuron will depend on the activation function used.