The equation Z = f [wn in ], where Z is the output, wn are weighting
functions, and in is a set of inputs describes:

A.
An artificial neural network (ANN)

B.
A knowledge-based system

C.
An expert system

D.
A knowledge acquisition system

Explanation:
The equation defines a single layer ANN as shown in Figure.

Each input, in, is multiplied by a weight, wn , and these products are
fed into a summation transfer function, , that generates an output,
Z. Most neural networks have multiple layers of summation and
weighting functions, whose interconnections can also be changed.
There are a number of different learning paradigms for neural networks,
including reinforcement learning and back propagation. In reinforcement
learning a training set of inputs is provided to the ANN
along with a measure of how close the network is coming to a solution.
Then, the weights and connections are readjusted. In back propagation,
information is fed back inside the neural network from the
output and is used by the ANN to make weight and connection
adjustments.
*Answers An expert system and A knowledge-based system are distracters that describe systems
that use knowledge-based rules of experts to solve problems using an
inferencing mechanism.
*A knowledge acquisition system refers to the means of identifying and acquiring the knowledge to
be entered into the knowledge base of an expert system.