Neuroinformatics I: Fuzzy Neural Networks of More-Equal-Less Logic (Static)
Neuroinformatics I: Fuzzy Neural Networks of More-Equal-Less Logic (Static)

Summary/Abstract: This article analyzes the possibilities of neural nets composed of neurons -- the summators of continuously varied impulse frequencies characterized by non-linearity ${N}$, when informational operations of fuzzy logic are performed. According to the facts of neurobiological research the neurons are divided into stellate and pyramidal ones, and their functional-static characteristics are presented. The operations performed by stellate neurons are characterized as qualitative (not quantitative) informational estimations ``more'', ``less'', ``equal'', i.e., they function according to ``more-equal-less'' (M-E-L) logic. Pyramidal neurons with suppressing entries perform algebraic signal operations and as a result of them the output signals are controlled by means of universal logical function ``NON disjunction'' (Pierce arrow or Dagger function). It is demonstrated how ste\-llate and pyramidal neurons can be used to synthesize the neural nets functioning in parallel and realizing all logical and elementary algebraic functions as well as to perform the conditional controlled operations of information processing. Such neural nets functioning by principles of M-E-L and suppression logic can perform signals' classification, filtration and other informational procedures by non-quantitative assessment, and their informational possibilities (the amount of qualitative states), depending on the number $n$ of analyzing elements-neurons, are proportional to $n!$ or even to $(2^{n})* n!$, i.e., much bigger than the possibilities of traditional informational automats functioning by binary principle. In summary it is stated that neural nets are informational subsystems of parallel functioning and analogical neurocomputers of hybrid action.

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