Write a formula describing the function defined by a one-hidden-layer (already trained) MLP with a single output. Also, write the formula describing the function defined by a RBFN with a single output. How do they differ?

A one-hidden-layer Multi-Layer Perceptron (MLP) and a Radial Basis Function Network (RBFN) are both neural network architectures used in soft computing and have distinct mathematical formulations. Let's describe the function defined by each of them:

1. One-Hidden-Layer MLP:
A one-hidden-layer MLP with a single output can be represented mathematically as follows:

Output (y) = Σ (w_i * φ(a_i * x + b_i))

Here, the terms have the following meanings:

- Output (y): The final output of the network, which is a weighted sum of the hidden layer activations.
- w_i: The weight associated with the ith hidden unit's output.
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