Not currently offered

Credit points: 15

Subject outline

Quantitative analysis plays an important role in business analytics and knowledge engineering, thus it is very useful to develop computing skills for data regression and classification. This subject covers some fundamentals of computational intelligence techniques, including linear regression analysis, fuzzy inference systems, neural networks and hybrid neuro-fuzzy systems. The subject is designed with a focus on solving time-series forecasting problems using fuzzy inference systems, where fuzzy inference mechanisms and fuzzy rule extraction from numerical data are addressed. Some advanced learning techniques for training neural networks will also be highlighted. In labs and assignment students will work with business datasets for time-series prediction using a fuzzy system and neural networks with advanced learning algorithms, which help to consolidate the knowledge taught in the lectures and gain a hands-on experience on computational intelligence applications in business.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorJustin Wang

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

Prerequisites CSE4DBF or admission into one of the following courses: SMIT, SMICT,SMITCN SMCSC, SGIT, SGCS or SMDS


Incompatible subjectsCSE3CI OR CSE4CI

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsStudents in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems

Resource TypeBook

Resource RequirementRecommended

AuthorC. T. Lin, C. S. Lee





Chapter/article titleN/A



Other descriptionN/A

Source locationN/A

Artificial intelligence-a guide to intelligent systems

Resource TypeOther resource

Resource RequirementPrereading

AuthorM. Negnevitsky


Edition/Volume3 edition

PublisherAddison Wesley


Chapter/article titleN/A



Other descriptionN/A

Source locationN/A

Career Ready


Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Explain the technologies and applications of computational intelligence systems (CIS for data analytics.
02. Describe the major components and system design in developing computational intelligence systems for data modelling i.e., data regression and classification.
03. Apply learnt knowledge and skills to analyse data, design, implement and evaluate computational intelligence systems for real world problem solving
04. Implement a fuzzy expert system and a neural network for time-series forecasting with real world data from industry.

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