Rule-based fuzzy systems

In order to process the input to obtain the output reasoning, there are six steps involved in the creation of a rule based fuzzy system ... 

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. 

The architecture of an ANFIS model is shown in Figure 14.4. As can be seen, the proposed neuro-fuzzy model in ANFIS is a multilayer neural network-based fuzzy system, which has a total of five layers. The input (layer 1) and output (layer 5) nodes represent the descriptors and the response, respectively. Layer 2 is the fuzzification layer in which each node represents a membership. In the hidden layers, there are nodes functioning as membership functions (MFs) and rules. This eliminates the disadvantage of a normal NN, which is difficult for an observer to understand or to modify. The detailed description of ANFIS architecture is given elsewhere (31). 

Figure 8.2.2 Overlapping categories of fuzzy sets, which can be modified according to the different kinds of data and the uncertainties. The higher the uncertainties, the larger the overlap between sets. The degree of membership of a data point to one or more classes is the input into rule-based expert systems, which enable the combination of different kinds of data (Heise and Forstner, 2007). Reproduced by permission of the Royal Society of Chemistry...

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. 

Intelligent product cross-selling system in fashion retailing using radio frequency identification (RFID) technology, fuzzy logic and rule-based expert system... 

RFID, retailing, fuzzy logic, rule-based expert system. 

Intelligent product cross-selling based on rule-based expert system and fuzzy screening technique... 

Mendel JM (2001) Uncertain rule-based fuzzy logic systems introduction and new directions. Prentice Hall, Englewood Cliffs, NJ... 

One variation of rule-based systems are fuzzy logic systems. These programs use statistical decision-making processes in which they can account for the fact that a specific piece of data has a certain chance of indicating a particular result. All these probabilities are combined in order predict a final answer. 

Chen et al. [24] provide a good review of Al techniques used for modeling environmental systems. Pongracz et al. [25] presents the application of a fuzzy-rule based modeling technique to predict regional drought. Artificial neural networks model have been applied for mountainous water-resources management in Cyprus [26] and to forecast raw-water quality parameters for the North Saskatchewan River [27]. 

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... 

A rule based approach to process control has for many years provided an alternative to traditional methods in the form of fuzzy logic control (8,9). Since the advent of expert systems, rulebases have been used for fault diagnosis [10], to advise operators (11) 9 to aid control engineers when installing PID controllers (12), to provide expert on-line tuning for PID controllers (13), and to control processes without the use of fuzzy logic (14,15). 

Data of different units and uncertainties can be combined using IF... THEN... rules, based on expert knowledge. Recently, the application of the traceability concept on ecotoxicological studies has been described (Ahlf and Heise, 2007). A suggestion for an ecotoxicological classification system for sediments based on fuzzy sets and fuzzy expert systems is under development (see Chapter 6.2). 

Fuzzy inference systems are also known as fuzzy associative memories, fuzzy models, fuzzy-rule-based systems, or fuzzy controllers. 

We learned the fundamentals about inferences on rule-based systems in Section 8.1 on symbolic knowledge processing. One possibility to infer on the knowledge represented in the computer is based on IF-THEN rules. In fuzzy logic, the premises and consequences are described by a fuzzy relation. Consider the following rules ... 

Tagaki and Sugeno suggested rule-based systems that involve fuzzy sets only in the premise part and the consequence part consists of a nonfuzzy function, for example,... 

Here, the premise is described by a membership function for the linguistic variable high and the function for the detection limit is the sum of the blank signal, y, and three times the standard deviation of the blank signal, Sg, (cf. Eq. (4.3)). Optimization of the parameters in the premise part of the rules is adaptively done by combining the fuzzy rule-based system with a neural network. Consider an adaptive neuro-fuzzy system with two inputs, and... 

Compare symbolic knowledge processing in case of crisp and fuzzy-rule-based systems. 

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