Fuzzy logic -based models

R. Jayachandran, S. Denis Ashok, S. Narayanan, Fuzzy Logic based modelling and simulation approach for the estimation of tire forces, Procedia Engineering, ISSN 1877-7058 64 (2013) 1109-1118. http //dx.doi.Org/10.1016/j.proeng.2013.09.189. 

FUZZY LOGIC-BASED MODELS 9.3.1 Introduction to Fuzzy Logic... 

M. Ulieru and R. Isermann. Design of a fuzzy-logic based diagnostic model for technical processes. Fuzzy Sets and Systems, 58 249-271, 1993. 

FAMs map fuzzy sets (inputs) to fuzzy sets (outputs). The output fuzzy sets should be defuzzified into numerical values to control the plant. Due to the powerful ability of fuzzy sets in describing system linguistic and qualitative behavior and imprecise and/or uncertain information, many industrial process behavior and control laws can be modeled by fuzzy logic-based approaches. Fuzzy logic has been applied in a wide range of automated systems, including ... 

SUGENO, M. and Yasukawa, T., 1993, A fuzzy-logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1, pp. 7-31. 

A series of monographs and correlation tables exist for the interpretation of vibrational spectra [52-55]. However, the relationship of frequency characteristics and structural features is rather complicated and the number of known correlations between IR spectra and structures is very large. In many cases, it is almost impossible to analyze a molecular structure without the aid of computational techniques. Existing approaches are mainly based on the interpretation of vibrational spectra by mathematical models, rule sets, and decision trees or fuzzy logic approaches. 

Knowledge based approaches such as fuzzy logic, neural networks or multiagents model currently constitute an important axis of research and application in bioprocesses. They have shown their usefulness particularly when one does not have an analytical model but that a certain expertise is available. Harmand and Steyer [37] have addressed that when this expertise comprises a sufficiently important know-how, approaches such as fuzzy logic will be preferred. If, on the other hand, one has only a limited experience but lays out of a rather important data base, the statistical approaches such as neural networks can be used. 

In order to calcnlate the individual and societal risks that are relevant to solvent selection, mathematical models must be used. In a fuzzy logic approach, the characterization of the effects of solvent use is strictly connected with the fuzzy representation of the input variables, that are based on individual risk and societal risk (Bonvicini et al., 1998). 

In the fluid-bed granulation process, moisture control is the key parameter that needs to be controlled. Faure et al. (133) have used process control for scale-up of a fluidized bed process. They used infra-red probes to monitor moisture. As there are normally large numbers of inter-related variables, they used computerized techniques for process control, such as fuzzy logic, neural networks, and models based on experimental techniques. 

The second way to handle knowledge-based guidance is imbedded in both the forward and the inverse model development but typically has fuzzy logic parameters. Now, there you do need some tuning. But you are not bound by that mix alone. That is why it works to first sit down and interact and then overrule where the direction the system is moving. 

Control based on neural network. Similar to fuzzy logic modeling, neural network analysis uses a series of previous data to execute simulations of the process, with a high degree of success, without however using formal mathematical models (Chen and Rollins, 2000). To this goal, it is necessary to define inputs, outputs, and how many layers of neurons will be used, which depends on the number of variables and the available data. 

Fuzzy logic modeling. A linguistically interpretable rule-based model, based on the available expert knowledge and measured data. 

Parameter determination is accomplished by laboratory measurements or by a least-mean-squares method if reference data are available. With such methods, it is possible to use a current measurement with a low accuracy. This can reduce costs for a SoC measurement capability. Singh et al. [18] and Heinemann [19] describe SoC determination methods based on fuzzy logic. This approach relies on the use of expert knowledge in lieu of complex mathematical models. It is also possible to combine a fuzzy SoC algorithm with other methods to increase the accuracy. 

The problems encountered in mathematical modeling of tumble/growth agglomeration do not relate to the theories, formulas, and possibilities to solve the ever more complicated equations. With modem computing possibilities, a whole series of assumptions can be introduced into the model equations and responses to certain imaginary process conditions can be predicted. However, the real system often produces unexpected results intermittently or even consistently without offering a clear indication of why such deviations occur. Introduction of new mathematical methods, such as, for example, fuzzy logic or chaos theory, produce more complicated model equations and closer to life results but still are not able to serve as unequivocal bases for control schemes. 

Fig. 4.14 Plot of the results of a calculation of the steady-state concentration of frnctose 6-phosphate for the system shown in fig. 4.13. The enzyme models are either based on Michaelis-Menten formalisms or modifications of multiple allosteric effector equations. The gate exhibits a function with both AND and OR properties. At low concentrations of both inpnts, the mechanism functions similarly to an OR gate, while at simultaneously high concentrations of the inpnt species (citrate and cAMP), the output behavior more closely resembles a fuzzy logic AND gate. The mechanism satisfies the requirements for a fuzzy aggregation function. (From [7].)...

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