Page 251 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 13: Expert System Architecture

Notes
Figure 13.4: Architecture

An ANN is typically defined by three types of parameters:
1. The interconnection pattern between different layers of neurons

2. The learning process for updating the weights of the interconnections
3. The activation function that converts a neuron’s weighted input to its output activation.
Mathematically, a neuron’s network function f(x) is defined as a composition of other functions
g (x), which can further be defined as a composition of other functions. This can be conveniently
i
represented as a network structure, with arrows depicting the dependencies between variables.
A widely used type of composition is the nonlinear weighted sum, where f(x) = K(Σ w g (x)), where
i i i
K (commonly referred to as the activation function) is some predefined function, such as the
hyperbolic tangent. It will be convenient for the following to refer to a collection of functions g
i
as simply a vector g = (g , g ,…,g ).
1 2 n
Figure 13.5: ANN Dependency Graph

This Figure 13.5 depicts such a decomposition of f, with dependencies between variables indicated
by arrows. These can be interpreted in two ways.
The first view is the functional view: the input x is transformed into a 3-dimensional vector h,
which is then transformed into a 2-dimensional vector g, which is finally transformed into f. This
view is most commonly encountered in the context of optimization.

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