In a multilayer perceptron neural network with three interval inputs and one output, how many weights, including biases, are estimated?

In a multilayer perceptron (MLP) neural network, the number of weights and biases can be calculated based on the architecture of the network, specifically the number of inputs, hidden layers, hidden layer neurons, and the output.

For the given scenario with three interval inputs and one output, let’s assume a simple structure where there's at least one hidden layer. Each hidden layer neuron will have a weight associated with every input plus a bias term.

1. Input Layer to Hidden Layer Weights: If we denote the number of hidden neurons as (n), then the number of weights connecting the input layer to the hidden layer is (3n) (three inputs for each of the (n) hidden neurons). Additionally, each of the (n) neurons has one bias weight, resulting in an additional (n) weights. Therefore, the total weights from the input layer to the hidden layer is (3n + n = 4n).

2. Hidden Layer to Output Weights: Moving to the output layer, if there is only one output, there would be (n) weights from the hidden layer to the output plus one bias term. This means the weights from the hidden layer to