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Abbreviations
- Adaptive system:
-
A system that can be automatically modified or reorganized to meet a set of performance specifications.
- Artificial intelligence (AI):
-
Computer-generated intelligence or characteristic (externally displayed) of a man-made system that behaves like a naturally intelligent system (biological system) to some degree.
- Backpropagation:
-
This is a “learning by example” or “supervised learning” technique of a neural network. Using previously obtained input-output data (training data) of a system, a neural network is trained by assigning the training output data to the network output and then computing the corresponding signals in the previous layers of the network, sequentially. The error is computed and minimized in this manner.
- Defuzzification:
-
The process of converting a fuzzy quantity into a crisp (non-fuzzy) quantity. One method, called the “centroid defuzzification,” determines the centroid of the membership function of the fuzzy quantity and uses it as the crisp representation of the fuzzy quantity.
- Evolutionary computing:
-
A nonanalytical optimization technique that mimics biological evolution. Based on an optimization function (fitness function), solutions are selected by “mating” good solutions to create better solutions and discarding poor solutions.
- Expert systems:
-
Computer-based intelligent systems which take in data, match to a knowledge base, and generate inferences (solutions, advice, prescriptions, predictions, etc.) in a manner that somewhat mimics a human expert. Expert systems are developed by gathering human expertise in a specific domain and properly representing it in a computer system with a human interface.
- Fuzzification:
-
The process of converting a crisp quantity into a fuzzy quantity. A membership function is determined to “fuzzify” the crisp quantity. For example, the crisp quantity may form the peak value or centroid of a membership function of sufficient width.
- Fuzzy control:
-
A model-free control technique where control knowledge is represented by “if-then” statements that contain qualitative or “fuzzy” terms such as “small” and “fast” as present in human statements. System’s outputs are observed (or measured) and “matched” with the control knowledge base to arrive at control actions.
- Fuzzy logic:
-
A logic that is more generalized than binary crisp logic. Instead of the two states in binary logic, multiple states are possible, with a degree of overlap among states. Here, for example, the state of “warm” and the state of “hot” have some overlap, as is common with human perception.
- Fuzzy set:
-
In the same manner that binary logic and Boolean sets go together, fuzzy logic and fuzzy sets are related. A fuzzy set has a non-crisp boundary. Elements that fall on the boundary have some level of presence within the set and a complementary level of presence outside the set.
- Genetic algorithms:
-
A nonanalytical optimization technique that mimics biological evolution. Based on an optimization function (fitness function), solutions are selected by “mating” good solutions to create better solutions and discarding poor solutions.
- Intelligent control:
-
A control approach that mimics a human who has expertise to generate a suitable control action for a particular system. A common approach uses a control knowledge base and an inferencing mechanism which matches observed/measured information with the knowledge base to generate the control action (controller output or control input to the system).
- Intelligent machine:
-
Machine with an artificial (computer-generated) brain, so that it can behave like a naturally intelligent biological system. It uses techniques of artificial intelligence (AI) for this purpose. Often, the term “machine” refers to a computer. More appropriately, the machine is a physical device that performs an engineering task, and its brain is a computer with AI software.
- Knowledge-based system:
-
An artificially intelligent system that uses a knowledge base (KB) to represent expert knowledge in a particular application domain. A decision-making method (inference engine) is used to “match” data or observed information with the KB to generate inferences.
- Membership function:
-
A function that represents a fuzzy set. In this function, the membership of a quantity in a fuzzy set is represented by a numerical value between 0 and 1. Set elements that are clearly within the set are represented by a membership grade of 1 and elements that are clearly outside the set are represented by a membership grade of 0. Elements that lie on the set boundary have membership grades between 0 and 1, as determined by the degree of membership in the set.
- Neural networks:
-
Massively parallel networks of artificial neurons that represent highly nonlinear systems or processes, without using analytical models. By adjusting the network parameters, the behavior of the network is made to resemble the actual system. It somewhat resembles the activity of a biological brain.
- Probabilistic system:
-
A system that possesses a degree of randomness or uncertainty and uses methods that involve probability distribution functions to generate decisions, actions, or estimates.
- Soft computing:
-
An approach that uses one or more techniques of fuzzy logic, neural networks, evolutionary computing, and probabilistic methods to perform numerical operations by somewhat mimicking biological systems.
Bibliography
Primary Literature
Cao Y, de Silva CW (2006a) Dynamic modeling and neural-network adaptive control of a deployable manipulator system. J Guidance Control Dynamics 29(1):192–195
Cao Y, de Silva CW (2006b) Supervised switching control of a deployable manipulator system. Int J Control Intell Syst 34(2):153–165
de Silva CW (1992) Research laboratory for fish processing automation. Int J Robot Comput Integr Manuf 9(1):49–60
de Silva CW (1997) Intelligent control of robotic systems with application in industrial processes. Robotics Auton Syst 21:221–237
de Silva CW (2003) The role of soft computing in intelligent machines. Philos Trans Royal Soc Ser A UK 361(1809):1749–1780
de Silva CW, Wickramarachchi N (1997) An innovative machine for automated cutting of fish. IEEE/ASME Trans Mechatron 2(2):86–98
Filippidis A, Jain LC, de Silva CW (1999) Intelligent control techniques. In: Jain LC, de Silva CW (eds) Intelligent adaptive control. CRC, Boca Raton
Goulet JF, de Silva CW, Modi VJ (2001) Hierarchical control of a space-based deployable manipulator using fuzzy logic. AIAA J Guid Control Dyn 24(2):395–405
Rahbari R, de Silva CW (2001) Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator. Eng Appl Artif Intell 14(6):763–784
Rahbari R, Leach BW, Dillon J, de Silva CW (2005) Expert system for an INS/DGPS integrated navigator installed in a bell 206 helicopter. Eng Appl Artif Intell 18(3):353–361
Tang PL, Poo AN, de Silva CW (2001) Knowledge-based extension of model-referenced adaptive control with application to an industrial process. J Intell Fuzzy Syst 10(3–4):159–183
Wang Y, de Silva CW (2008) A machine learning approach to multi-robot coordination. Eng Appl Artif Intell 21(3):470–484
Yan GKC, de Silva CW, Wang GX (2001) Experimental modeling and intelligent control of a wood-drying kiln. Int J Adapt Control Signal Process 15:787–814
Zadeh LA (1984) Making computers think like people. IEEE Spectrum 21(8):26–32
Books
Anderson TW (1984) An introduction to multivariate statistical analysis. Wiley, New York
Davis L (1991) Handbook on genetic algorithms. Van Nostrand-Rienhold, New York
de Silva CW (1995) Intelligent control: fuzzy logic applications. CRC, Boca Raton
de Silva CW (2000) Intelligent machines: myths and realities. CRC, Boca Raton
de Silva CW (2005) Mechatronics: an integrated approach. Taylor & Francis/CRC, Boca Raton
Dubois D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic, Orlando
Farrell JA, Polycarpou MM (2006) Adaptive approximation based control. Wiley-Interscience, New York
Forsyth R (1984) Expert systems. Chapman & Hall, New York
Gupta MM, Rao H (1994) Neural control theory and applications. IEEE Press, Piscataway
Hart A (1986) Knowledge acquisition for expert systems. McGraw-Hill, New York
Jain LC, de Silva CW (1999) Intelligent adaptive control: industrial applications. CRC Press, Boca Raton
Jang JSR, Sun CTS, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall, Upper Saddle River
Karray FO, de Silva CW (2004) Soft computing and intelligent systems design – theory, tools, and applications. Addison Wesley, New York
Klir GJ, Folger TA (1988) Fuzzy sets, uncertainty, and information. Prentice Hall, Englewood Cliffs
Pao YH (1989) Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading
Passino KM (2005) Biomimicry for optimization, control, and automation. Springer, London
Ruano AE (2005) Intelligent control systems using computational intelligence techniques. The Institute of Electrical Engineers, Herts
Vonk E, Jain LC, Johnson RP (1997) Automatic generation of neural network architecture using evolutionary computing. World Scientific, Singapore
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de Silva, C.W. (2013). Intelligent Control. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-3-642-27737-5_288-2
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DOI: https://doi.org/10.1007/978-3-642-27737-5_288-2
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