To address non-linearly separable problems, which type of neural network architecture is commonly employed? a. Single-layer perceptron network b. Multilayer feedforward neural network c. Radial Basis Function network d. Recurrent neural network
Question
To address non-linearly separable problems, which type of neural network architecture is commonly employed? a. Single-layer perceptron network b. Multilayer feedforward neural network c. Radial Basis Function network d. Recurrent neural network
Solution
b. Multilayer feedforward neural network
This type of neural network architecture is commonly employed to address non-linearly separable problems. The reason is that a single-layer perceptron can only solve linearly separable problems. On the other hand, a multilayer feedforward neural network, also known as a multilayer perceptron (MLP), can solve non-linear problems. It does this by having multiple layers of nodes (neurons) in a directed graph, with each layer fully connected to the next one. Each node in a layer (except for the input layer) uses a non-linear activation function, which allows the network to represent non-linear relationships between its inputs and outputs.
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