Sensors, fuzzy logic

Fuzzy logic control Longitudinal combustion instabili- 1. Only effective when many states can be sensored... 

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. 

Wu and Joseph [35] incorporated fuzzy logic into a knowledge-based control system for control of composite curing. They used the fuzzy logic to interpret the sensors and adjust the amount of control reaction on a simulated process. Even though the limitations of the simulator used did not allow full evaluation of the advantages of this system, it did show that the controller could react to material and process variations and improve the process plan. 

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. 

Another method is the so-called Kalman filter [ 118], [ 119]. Iterative calculations enable the spectrum of an unknown component to be determined using a sufficiently large number of calibrating wavelengths. Methods of multicomponent analysis by fuzzy logic have not proved very successful [106]. In contrast, a method using Fourier transforms followed by multicomponent analysis is available commercially [120]. The most recent methods, using neuronal networks [6], [121], are used much less in classical multicomponent analysis [ 122] than in sensor arrays with relatively low selectivity, where they are employed to determine relative concentrations in gas mixtures or liquids [123], [124]. Another wide field of application. in the infrared, is in foodstuffs analysis and pharmaceutical chemistry [125]. 

Agapito et al. ° applied fuzzy logic to an array of semiconductor gas sensors to analyze different atmospheres for the different gases and Otto et al. developed a scheme based on the principles of fuzzy logic that makes use of various pieces of information available either from spectroscopic knowledge or from the particular spectrum that requires evaluation. A fuzzy expert system has also been successfully developed for the automated qualitative and semiquantitative interpretation of X-ray diffraction spectra/ automation of matrix-assisted laser desorption-ionization mass spectrometry (MALDI), and for polymer analysis. 

Maziarz, W., Potempa, P., Sutor, A., and Pisarkiewicz, T. Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic. Thin Solid Films 2003,436, 127-131. 

Agapito et al. performed semi-quantitative estimation of NO in presence of CO and water vapor by semiconductor gas sensor arrays and fuzzy logic. The measured value of the NO concentration has to be corrected due to interference in the... 

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