Intelligent_Systems - LECTURE NOTES ON INTELLIGENT SYSTEMS...

Info icon This preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
LECTURE NOTES ON INTELLIGENT SYSTEMS Mihir Sen Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556, U.S.A. December 4, 2002
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2
Image of page 2
Contents Preface 7 1 Introduction 9 1.1 Intelligent systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Related disciplines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Systems theory 11 2.1 Mathematical models . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Use of mathematical models . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.1 System response . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.2 Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.3 System identification . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Linear equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.1 Linear algebraic . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.2 Ordinary differential . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.3 Partial differential . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.4 Integral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.5 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Nonlinear systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.1 Algebraic equations . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.2 Ordinary differential equations . . . . . . . . . . . . . . . . . . 23 2.5.3 Bifurcations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.6 Cellular automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.7 Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.7.1 Linear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.7.2 Nonlinear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2.8 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.8.1 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.8.2 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.8.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.9 Intelligent systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.9.1 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.9.2 Need for intelligent systems . . . . . . . . . . . . . . . . . . . 33 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Artificial neural networks 35 3.1 Single neuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Network architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Single-layer feedforward . . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Multilayer feedforward . . . . . . . . . . . . . . . . . . . . . . 37 3.2.3 Recurrent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.4 Lattice structure . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3 Learning rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1 Hebbian learning . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.2 Competitive learning . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.3 Boltzmann learning . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3.4 Delta rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.4 Multilayer perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.4.1 Feedforward . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4.2 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4.4 Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Radial basis functions . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.6 Other examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.7 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.7.1 Heat exchanger control . . . . . . . . . . . . . . . . . . . . . . 47 3.7.2 Control of natural convection . . . . . . . . . . . . . . . . . . 47 3.7.3 Turbulence control . . . . . . . . . . . . . . . . . . . . . . . . 47 4 Fuzzy logic 49 4.1 Fuzzy sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Mamdani method . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Takagi-Sugeno-Kang (TSK) method . . . . . . . . . . . . . . . 51 4.3 Defuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Fuzzy reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4
Image of page 4
4.5 Fuzzy-logic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.6 Fuzzy control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.7 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.8 Other applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5 Probabilistic and evolutionary algorithms 57 5.1 Simulated annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Genetic algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.3 Genetic programming . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4.1 Noise control . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4.2 Fin optimization . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4.3 Electronic cooling . . . . . . . . . . . . . . . . . . . . . . . . . 59 6 Expert and knowledge-based systems 61 6.1 Basic theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 7 Other topics 63 7.1 Hybrid approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 7.2 Neurofuzzy systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 7.3 Fuzzy expert systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 7.4 Data mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 8 Electronic tools 65 8.1 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.1.1 Digital electronics . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.1.2 Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.1.3 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.1.4 Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2 Computer programming . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2.1 Basic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2.2 Fortran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2.3 LISP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2.4 C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.2.5 Matlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 8.2.6 C++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 8.2.7 Java . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 8.3 Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 8.3.1 Workstations . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5
Image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
8.3.2 PCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 8.3.3 Programmable logic devices . . . . . . . . . . . . . . . . . . . 66 8.3.4 Microprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Bibliography 71 6
Image of page 6
Preface “Intelligent” systems form part of many engineering applications that we deal with these days, and for this reason it is important for mechanical and aerospace engineers to be aware of the basics in this area. The present notes are for the course AME 498I/598G Intelligent Systems given during the Fall 2002 semester to undergraduate seniors and beginning graduate students. The objective of this course is to introduce the theory and applications of this subject.
Image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 8
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern