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.