
Unit 40: 
Knowledgebased Systems and 



Techniques 


Unit code:

A/601/1446



QCF level:

5



Credit value:

15






• Aim
This unit will introduce learners to the concepts and techniques used in
artificial intelligence and knowledgebased systems and develop an
understanding of rulebased systems, fuzzy logic and artificial neural
networks.
• Unit abstract
The unit starts by introducing learners to knowledge bases and rule
bases that are used extensively in expert systems, and at a much lower level are
used for simple reasoning/logic operations. The concept of rule bases is
extended to fuzzy operations and fuzzy logic which is increasingly being used
in domestic appliances and is in use in many industrial applications. Finally,
learners are introduced to artificial neural networks, which are related to
basic brain (synapse) functions, and ‘learning’ is demonstrated using simple
neuron structures. Evaluation of fuzzy logic algorithms and artificial neural
networks is achieved via simulation using proprietary software.
• Learning outcomes
On successful completion of this unit a
learner will:
1 Understand the use of knowledgebased and
rulebased systems
2 Be able to use fuzzy logic
3 Be able to use artificial neural networks.
Unit content
1 Understand the use of
knowledgebased and rulebased systems
Knowledge
and rule base:
terminology (facts and rules, propositions or predicates, deep and surface
knowledge – heuristics); semantic networks; forward chaining; antecedents and
consequences; conflict resolution; backward chaining; applications and
implementation (identification of examples where such systems would be used)
2 Be able to use fuzzy logic
Human
analogy: human reasoning and
expert knowledge
Fuzzy logic theory: conventional binary logic; crisp and fuzzy sets; fuzzy reasoning;
fuzzy rules; membership functions; inference engines; defuzzification
Applications: identification and analysis of examples eg cameras, domestic
appliances, industrial equipment and processes
Implementation: development of fuzzy rules; evaluation of
performance via simulation
3 Be able to use artificial neural networks
Biological
analogy: synapse, axons,
dendrites
Network
topologies and operating characteristics: Hopfield networks; multilayer perceptron; back propagation;
self organising networks; Kohonen networks; radial basis function networks;
neurofuzzy and fuzzyneural
Applications: identification and analysis of examples eg pattern classification,
optical character recognition, image analysis, biometrics
Implementation: experimentation with neural network
configurations; learning coefficients: RMS; error evaluation of
performance via simulation
Learning outcomes and assessment criteria

Learning outcomes

Assessment criteria for pass




On successful completion of

The learner can:




this unit a learner will:












LO1 Understand the use of


1.1

explain knowledgebase and rulebase
terminology



knowledgebased
and rule


1.2

devise and interpret semantic networks



based
systems













1.3

describe applications of knowledgebased
and rule






based systems









LO2 Be able to use fuzzy logic


2.1

describe human reasoning and expert
knowledge





2.2

use fuzzy logic theory to produce fuzzy
rules,






fuzzification and defuzzification





2.3

describe and evaluate applications of fuzzy
logic





2.4

design and evaluate fuzzy logic systems
using






appropriate software









LO3 Be able to use artificial


3.1

explain the biological analogy of neural
networks



neural
networks


3.2

explain network topologies and operating
characteristics










3.3

describe and evaluate applications of
neural networks





3.4

design and evaluate neural networks using appropriate






software.








Guidance
Links
This is a standalone unit.
Essential requirements
The use of software packages is an essential
part of the delivery of this unit. Proprietary software such as
MATLAB/Simulink, or equivalent, with appropriate tool boxes for fuzzy logic and
neural networks must be available to learners.
Employer engagement and vocational contexts
The delivery
of this unit will benefit from centres establishing strong links with employers
willing to contribute to the delivery of teaching, workbased placements and/or
detailed case study materials.
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