JNTU B.Tech III ARTIFICIAL NEURAL NETWORKS Previous Question Paper (March - 2024 - R16)

The human brain is a biological system with approximately 10¹¹ neurons and 10¹⁴ synapses, operating in parallel with massive connectivity and high fault tolerance. In contrast, an artificial neural network is a mathematical model implemented in software or hardware that simulates these processes using simplified nodes (neurons) and weighted connections. While the brain is highly adaptive, energy-efficient, and capable of complex cognitive functions, artificial neural networks are specialized for specific tasks like pattern recognition and function approximation, relying on iterative training algorithms rather than biological plasticity.

Error correction learning is a supervised learning paradigm where the adjustment of synaptic weights in a neural network is driven by the difference between the actual output and the desired target output. This difference, known as the error signal, is used to iteratively modify the weights to minimize the error over time.

The learning process typically follows these steps:

• An input vector is applied to the network to produce an actual output.
• The actual output is compared with the target output to calculate the error signal.
• The weights are adjusted according to a learning rule (such as the Delta rule or backpropagation) to reduce the error for the next iteration.

The objective is to converge the network to a state where the error signal is minimized or reaches an acceptable threshold, effectively allowing the network to learn the underlying mapping of the training data.