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  1. 1. Module Title Intelligent Systems Module Code COM623M1A Module Level 3 Credit Points 20 Semester 1 Module Status Option Location Magee Prerequisite(s) Mathematics 1 Corequisite(s) DR Girijesh Prasad , School of Computing and Intelligent Module Coordinator Systems; Magee Teaching Staff Dr Girijesh Prasad Responsible Contact Hours Lectures 36 hours Tutorials 12 hours Practicals 12 hours Assignment prep. 36 hours Directed Reading 44 hours Private Study 60 hours Academic Topic Electronics and Computing Rationale The module is designed to introduce the student to the research area of intelligent techniques such as fuzzy logic, neural networks and genetic algorithms. This module is structured in four parts. The first introduces the student to the methods of approximate reasoning and fuzzy systems. The second part concentrates on neural networks and the learning algorithms required. The third part describes the area of optimization and search routines using genetic algorithms. The final part describes hybrid approaches involving combinations of the three techniques. The emphasis of the module is on the design and implementation of intelligent technologies. This will involve practical implementations through a software approach using the Matlab environment. Aims The aims of the module are to introduce final year students to the research domain of Intelligent systems and to provide both a theoretical and practical description of how such systems are designed and implemented.
  2. 2. Learning Outcomes Upon the successful completion of this module a successful student will be able to: Understand, design and implement an approximate reasoning system using fuzzy (i) logic (ii) Understand, design and implement a learning system using neural networks Understand, design and implement an optimisation and search system using (iii) genetic algorithms Understand, design and implement a hybrid intelligent system which combines (iv) the complementary aspects of each technology Content Fuzzy Logic Notion of approximate reasoning and fuzzy logic, Fuzzy sets, Membership functions, Operations on fuzzy sets, Extension principle, Fuzzy relations. Steps in fuzzy reasoning: Fuzzification, Inferencing, Implication and Defuzzification, 1 Linguistic variables and hedges, Theory of approximate reasoning, Fuzzy rule-based, Fuzzy reasoning mechanisms. Software and hardware implementation of fuzzy systems, Effectiveness and limitations of fuzzy systems. Applications in computing and engineering: Fuzzy control, fuzzy clustering etc Neural Networks Bio-inspired origins. Biological neurons and artificial neural models, Classification of neural networks; Network learning rules: Hebbian, Perceptron, Delta, Widrow-Hoff learning rule, Multilayer feed-forward networks, 2 Backpropagation learning; Self-organising Maps. Recurrent networks: Elman, Hopfield networks. Hardware and software implementation of NN. Effectiveness and limitations of neural network systems. Applications of NN in computing and engineering: Pattern recognition and business prediction. Genetic Algorithms Terminology and bio-inspired origins. Features of a GA: Chromosome and different representation scheme, fitness measure, population size, evaluation, 3 selection, crossover/recombination, mutation, replacement, and convergence. How do Gas work and comparison with other search techniques. Effectiveness and limitations of Gas. Applications of GA in computing and engineering: Parameter optimization. Hybrid approaches: Soft computing, Computational Intelligence, Artificial Life Rationale to why techniques can be combined and the advantages and 4 disadvantages. Description of the various hybrid combinations including their effectiveness and limitations. Practical applications in electronics and computing Learning and Teaching Methods
  3. 3. Six hours per week are used for lectures and tutorials and practical. The Tutorials will be tutor-led problem-solving sessions, solving assigned problems in groups and singly, etc. Coursework takes a variety of forms including open book tests and assigned problems to solve. The module will use a series of cases studies related to each part of the course to develop understanding of the subject material. The students will employ the Matlab simulation tool, an industry standard simulation package provided by Maths Works Inc. Assessment. Coursework Assignments 25% Written Examination 75% Course work CA1 A one hour, open book, multiple choice test, in week 7/8 covering all topics of the first six weeks. Worth 50% of CA. Quick feedback helps to identify students' weaknesses and act as a guide for future revision. Full discussion of results and solution sheet given in week 8. This imprints fundamental concepts and provides feedback. CA2 A case study issued at the end of week 4 to be completed before week 11. Worth 50% of CA. The case studies enable students to investigate the theory presented in the lectures in a practical sense by performing simulations in Matlab. This tests the student’s powers of deeper understanding and analysis. The coursework measures the student's achievement of learning outcomes (i),(ii), and (iii) for the module. Examination A written examination lasting three hours is completed by the student at the end of the semester. This will consist of compulsory and optional questions. The examination measures the student's achievement in all of the four learning outcomes (i),(ii), (iii) and (iv) for the module. Overall Mapping of learning outcomes on BEng (Hons) Electronics and Computer Systems A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 D1 D2 D3 D4 D5 EX √ √ √ CW √ √ √ √ √ √ √ √ √ Overall Mapping of learning outcomes on BSc (Hons) Computer Science A A A A A C C C C C C D D D D D B1 B2 B3 B4 B5 B6 D6 1 2 3 4 5 1 2 3 4 5 6 1 2 3 4 5 EX √ √
  4. 4. Overall Mapping of learning outcomes on BSc (Hons) Computer Science CW √ √ √ √ √ √ √ √ √ Overall Mapping of learning outcomes on BSc (Hons) Computing Major A1 A2 A3 A4 A5 B1 B2 B3 B4 C1 C2 C3 C4 C5 D1 D2 D3 D4 D5 D6 EX √ √ CW √ √ √ √ √ √ √ √ √ Reading List. Recommended 1. A.P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley & Sons, Ltd., 2002. 2. JSR Jang et al, Neurofuzzy and soft computing, Prentice Hall, 1997. 3. Hagan, MT, Demuth HB, Beale, MH, Neural Network Design, Campus publishing service, Colorado University Bookstore at Boulder 36 UCB, Boulder Colorado, USA, 1996. 4. F.M. Ham, Principles of Neurocomputing for Science & Engineering, McGraw- Hill International Edition, 2001. 5. J.M. Mendel, Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall, 2001. 6. A.A. Hopgood, Intelligent Systems for Engineers and Scientists, CRC Press, London. 2000. 7. R. Callan, The Essence of Neural Networks, Prentice Hall Europe, 1999. 8. W Pedrycz, Computational Intelligence – an Introduction, CRC Press 1997. Indicative 1. Haykin, S.:Neural Networks, A Comprehensive Foundation, Macmillan, 1999. 2. Jace M. Zurada: Introduction to Artificial Neural Systems, PWS Publishing Company, 1995. 3. M.J. Patyra and D.M. Mlynek: Fuzzy logic –Implementation and Application, Wiley and Tubner, 1996 4. Kosko, Fuzzy Thinking, Harper Collins 1994 5. Cox, E, Fuzzy Systems Handbook, Academic Press 1993 6. Fogel, David B.. - Evolutionary computation : toward a new philosophy of machine intelligence. - New York : IEEE Press, 1995. - 0780310381. 7. David E. Goldberg: Genetic Algorithms in Search, Optimisation and Machine Learning, 1989.
  5. 5. Summary Description Having completed this module the student should have an understanding of the research area of intelligent techniques. The module will address important implementation issues and describe the benefits of intelligent techniques in practical applications.