Artificial intelligence fuzzy logic

Turek, M., Heiden, W., Riesen, A., Chhabda, T. A., Schubert, J., Zander, W., Krueger, P., Keusgen, M., and Schoening, M. J. (2009). Artificial intelligence/fuzzy logic method for analysis of combined signals from heavy metal chemical sensors. Electrochim. Acta 54(25), 6082-6088. 

The classical adaptive control scheme is shown in Figure 2.58. Its goal is to use online identification through artificial intelligence (Al), neural networks, and fuzzy logic to adapt the model to the actual process. Al and model predictive control (MPC) can tolerate inaccuracy and uncertainty in the model, and online training can continuously improve the model. 

The ANN is an artificial intelligent technique that has several distinct advantages over rule-based Expert System and Fuzzy Logic. The technique has been shown to be a feasible technique that estimates the material properties for the FGM design. The estimation accuracy is satisfactory. 

Systems such as the one illustrated in figure 23.2 will also incorporate artificial intelligence. The information from the sensors will be used with fuzzy logic and neural networking to enable decisions by individual controllers based on the input from multiple sensors. Such systems will also incorporate sensor self-testing, self-calibration, and fault correction, resulting in reliable, automated systems applicable to any process or production line. These systems will significantly affect the productivity and profitability of food, chemical, and pharmaceutical production. 

Recovery Computer systems may apply techniques of artificial intelligence (Al) (e.g., fuzzy rules, knowledge-based logic, neural networks) to improve the quality of activities. For example, a robot system may be recovered automatically from error conditions through decisions made by AI programs. 

Reddy, N. P., and Buch, O. (2000). Committee Neural N otks for Speaker Verification, Intelligent Engineering Systems through Artificial Neural Networks, Vol. 5, Fuzzy Logic and Evolutionary Programming (editors C. Dagli, A. Akay, C. Philips, B. Femadez, J. Ghosh), ASME Press, New York, 911-915. 

Ali, S. F., Ramaswamy, A. (2009a). Optimal fuzzy logic control for MDOF structural systems using evolutionary algorithms. EngineeringAppli-cations of Artificial Intelligence, 22(3), 407-419. doi 10.1016/j.engappai.2008.09.004... 

Fuzzy logic and neuronal networks are methods of artificial intelligence. With neuronal networks, an extensive analogy to the function of natural sense organs is obtained. 

Changing from logic model system to Artificial intelligence and fuzzy logic computer control system had contributed to saving production cost in terms of high efficient plant operation. 

Entemann, C. W. (2002) Fuzzy logic misconceptions and clarifications. In Artificial Intelligence Review, 17(1), 65-84. 

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