In need of Fractional Brownian Noise (FBM) vs Perlin Noise clarification

Perlin Noise is actually a method to calculate what you call Noise. It is a form of coherent noise computed as the interpolated normalized gradients of a pseudo-random underlying function (or pseudo-randomly generated gradients). The frequency controls the "sampling" distance of the gradients, so the higher the frequency, the smaller the sampling distance, and more gradients per unit of space. Amplitude controls the max and min values of the noise function.

Hugo Elias' page on Perlin Noise has a good description of the concept, the algorithm, etc. Perlin Noise FAQ explains the algorithm in a very intuitive manner.

Noise is a general term. Perlin Noise is only a type of coherent noise. But there is many different types with different characteristics.

Fractional Brownian motion is, in fact, what you call Perlin Noise. It is a combination of multiple steps of Perlin Noise (or another similar noise function), each with a different frequency and amplitude. In the context of procedural generation, the variation in frequency from a step to the next is called lacunarity. The variation in amplitude from a step to the next is called gain.

Look at this. This project on terrain generation gives a very good description of what fBm is and how it is computed.