Learn which statements about artificial neural networks (ANNs) are true. Explore the basic architecture of neural networks, including nodes, weighted inputs, and activation states.
Table of Contents
Question
Inferring basic architecture of ANN, which is true for neural networks?
A. It has set of nodes and connections
B. Each node computes it’s weighted input
C. Node could be in excited state or non-excited state
D. All of the above
Answer
D. All of the above
Explanation
All the provided options are correct descriptions of fundamental features of artificial neural networks (ANNs). Here’s a detailed breakdown:
It has a set of nodes and connections
Neural networks are composed of interconnected nodes (also called neurons) that mimic the structure of biological neural systems. These nodes are connected by edges, which represent synapses in the brain. Each connection has an associated weight that determines its influence on the network’s output.
Each node computes its weighted input
Each node in an ANN processes input by calculating a weighted sum of its inputs and applying an activation function.The activation function then determines whether the node’s output should be passed to the next layer.
Node could be in excited state or non-excited state
Nodes in a neural network can either be activated (excited state) or not activated (non-excited state), depending on whether their output surpasses a threshold value. This behavior is determined by the activation function, such as ReLU, sigmoid, or tanh, which introduces non-linearity into the network.
Why Option D is Correct
Since all three statements accurately describe key properties of neural networks:
- Nodes and connections form the structural framework.
- Weighted inputs drive computations at each node.
- Activation states determine how signals propagate through the network.
Thus, the correct answer is D. All of the above, as all these elements are integral to how ANNs function.
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