Fuzzy associative memory

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

Figure 6 shows a structure of applying fuzzy logic in control. First, two types of inputs must be obtained numerical inputs and human knowledge or rule extraction from data (i.e., fuzzy rules). Then the numerical inputs must be fuzzified into fuzzy numbers. The fuzzy rules consist of the fuzzy membership functions (knowledge model) or so-called fuzzy associative memories (FAMs). Then the... 

An important use of fuzzy methods for Data Mining is for classification. Associations between inputs and outputs are known in fuzzy systems as fuzzy associative memories or FAMs. A FAM system encodes a collection of compound rules that associate multiple input statements with multiple output statements We combine such multiple statements using logical operators such as conjunction, disjunction and negation. 

ANN = artificial neural network AR = approximate reasoning BAM = bidirectional associative memory FAM = fuzzy associative memory FL = fuzzy logic LP = linear programming OLS = ordinary least squares PCCF = partial cross correlation function SSC = spectrum-structure correlation sup = supremum = fuzzy addition = fuzzy subtraction O = fuzzy multiplication Q = fuzzy division Q = relation. 

One interesting paper [164] discussed a technique called fuzzy optimal associative memory (FOAM), used for background prediction of NIR spectra. This software yields better background scans for calculation of NIR spectra of glucose in plasma matrices (from single-beam data). The FOAM software is usually used in conjunction with PLS and/or some other complex algorithm. 

P. B. Harrington and B. W. Wabuyele, Fuzzy Optimal Associative Memory for Background Prediction of Near-Infrared Spectra, Appl. Spectrosc., 50(1), 34 (1996). 

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