Fuzzy expert systems

Discusses evolutionary algorithms, cellular automata, expert systems, fuzzy logic, learning classifier systems, and evolvable developmental systems... 

Expert system Fuzzy logic X-ray fluorescence analysis [8]... 

Fuzzy logic systems grew out of a desire to quantify rule-based expert systems. Fuzzy set theory had provided us with an effective framework for dealing with fuzzy information and for translating control strategies based on an expert knowledge into an automatic control strategy. 

Van Veen and De Loos-Vollebregt reviewed various chemometric procedures which had been developed during the last decade for ICP-OES analysis. In these procedures, data reduction by techniques such as digital filtering, numerical derivatives, Fourier transforms, correlation methods, expert systems, fuzzy logic, neural networks, PCA, PLS, projection methods, Kalman filtering, MLR and generalised standard additions were discussed. 

The application of fuzzy logic in chemistry concerns interpretation of expert systems, fuzzy control in chemical production or evaluation of qualitative or vague quantitative rather than exact quantitative causal dependencies. The main advantages of using fuzzy theory in an expert system have been given by Zimmermann. - These advantages are also valid for the other application fields mentioned above and can be summarized as follows ... 

The control law This is the information flow structure through which the manipulated variables are handled based on the measurements. The complexity of the control law is determined by the diversity of the control objective. As a result, the controller can be simple (on—off, proportional, proportional-integrated differential), more complicated adaptive model-based, empirical (expert systems), fuzzy or neural network-based. Detailed references on the various control systems applied on anaerobic digesters can be found in Boe (2006) and Find et al. (2003). 

Bayesian probability theory and methods that are based on fuzzy-set theory. The principles of both theories are explained in Chapter 16 and Chapter 19, respectively. Both approaches have advantages and disadvantages for the use in expert systems and it must be emphasized that none of the methods, developed up to now are satisfactory [7,11]. 

The next few steps are very similar to those required in any software project. One of the first stages is the clear definition of the knowledge domain. It must be clear which problems the expert system must solve. It is at this stage not the intention to define how this can be done. Clarity and specificity must be the major guides here. Fuzziness at this stage will, more than in classical software projects, have to be paid for later when different interpretations cause misunderstandings. Equally important is the clear definition of the end user(s). An expert system set up as decision support tool for professionals is totally different from an expert system that can be used as a training support for less professional people. 

M. Otto, Fuzzy expert systems. Trends Anal. Chem., 9 (1990) 69-72. 

Harrington, P. B. Fuzzy Rule-building Expert Systems Minimal Neural Networks. J. Chemometrics 1991, 5, 467 186. 

Like all early expert systems, DENDRAL and SHRDLU required exact knowledge to function. The way that expert systems work depends on whether the knowledge that they manipulate is exact ("The temperature is 86°C") or vague ("The temperature is high"). We shall first consider how an ES can use exact knowledge to provide advice. Methods for dealing with ill-defined information form the topic for the next chapter, which covers fuzzy logic. 

Fuzzy logic is often referred to as a way of "reasoning with uncertainty." It provides a well-defined mechanism to deal with uncertain and incompletely defined data, so that one can make precise deductions from imprecise data. The incorporation of fuzzy ideas into expert systems allows the development of software that can reason in roughly the same way that people think when confronted with information that is ragged around the edges. Fuzzy logic is also convenient in that it can operate on not just imprecise data, but inaccurate data, or data about which we have doubts. It does not require that some underlying mathematical model be constructed before we start to assess the data. 

In a conventional expert system, the only rules to fire are those for which the condition is met. In a fuzzy system, all of the rules fire because all are expressed in terms of membership, not the Boolean values of true and false. Some rules may involve membership values only of zero, so have no effect, but they must still be inspected. Implicitly, we assume an or between every pair of rules, so the whole rule base is... 

Fuzzy logic is often presented as an extension in books that cover expert systems. Few texts exist in which the applications of fuzzy logic to scientific problems are described, but several texts include more general discussions of the principles and practical implementation of this method. Among the best is Negnevitsky s text on intelligent systems.9... 

Negoita, C.V. (1985), Expert Systems and Fuzzy Systems, Benjamin/Cummings, Menlo Park, CA. 

Friedmann El, Ocampo-Friedmann R (1984) The Antarctic crytoendolithic ecosystem relevance to exobiology. Orig Life 14 771-776 Fritsen CH, Priscu JC (1998) Cyanobacterial assemblages in permanent ice covers on Antarctic lakes distribution, growth rate, and temperature response of photosynthesis. J Phytol 34 587-597 Furfaro R et al (12) (2007) The search for life beyond Earth through fuzzy expert systems. Planet Space Sci in press. 

Thus, multilinear models were introduced, and then a wide series of tools, such as nonlinear models, including artificial neural networks, fuzzy logic, Bayesian models, and expert systems. A number of reviews deal with the different techniques [4-6]. Mathematical techniques have also been used to keep into account the high number (up to several thousands) of chemical descriptors and fragments that can be used for modeling purposes, with the problem of increase in noise and lack of statistical robustness. Also in this case, linear and nonlinear methods have been used, such as principal component analysis (PCA) and genetic algorithms (GA) [6]. 

O. D. Chuk, V. Ciribeni, and L. V. Gutierrez, Froth collapse in column flotation a prevention method using froth density estimation and fuzzy expert systems. Minerals Engineering 18(5) (2005). 

That is why systems that can handle this kind of uncertainty are mandatory elements of an expert system. Uncertainty in expert systems can be handled in a variety of approaches certainty factors, fuzzy logic, and Bayesian theroy. 

Accuracy checks are generally performed by comparing measured data with data from certified reference materials. When measured data are not accurate because of relative or systematic errors, or a lack of precision (noise), the comparison between measured data and reference values cannot lead to any useful conclusion in an expert system. To process larger sets of potential source data for knowledge bases, a method must be used that takes inaccuracies as well as natural fuzziness of experimental data into account — ideally automatically and without the help of an expert. 

The general principle in fuzzy logic is that a reference value Xq is associated with a fuzzy interval dx, and experimental data within an interval of Xq dx are identified as reference data. Since natural, or experimental, data are always inaccurate, and the representation of knowledge is quite like that in fuzzy logic, expert systems have to use fuzzy logic or some techniques similar to fuzzy logic [33]. In a computer system based on the fuzzy logic approach, fuzzy intervals for reference values are defined a priori. 

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