Neural Networks Analysis and Synthesis of the Multidimensional Signals by More-Equal-Less Logic

The aim of this paper is to formulate non-formal more-equal-less (M-E-L) logic of the neural networks analysis and synthesis of the multidimensional signals for anticipatory control in living and organized systems. The signals analysis and synthesis structures are necessary for anticipation procedures in the more complex systems decision making. The possibilities of neural nets composed of neurons as the algebraic dot productors of continuously varied impulse frequencies characterized by diode non-linearity {N}, when informational operations of fuzzy logic are performed is analyzed. According to the facts of neurobiological research the neurons are divided into satellite and pyramidal ones, and their functional-static characteristics are presented. The operations performed by satellite 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 satellite 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 2ⁿ· n!, i.e., much bigger than the possibilities of traditional informational automats functioning by binary principle. Such neurostructures of analysis by synthesis carries signal-information procedures on the principal-factor components analysis methods.