Self-adapting fuzzy pulse neurolemma system and reasoning algorithm and learning algorithm

Abstract

The embodiment of the invention discloses a self-adapting fuzzy pulse neurolemma system which has the following structure: TT=(A, Np, Nr, syn, I, O), wherein A-{a} is a single letter set, NP={sigma p1, sigma p2,..., sigma pm} is a proposition neuron set, sigma pi is a proposition neuron, i is larger than or equal to 1 and smaller than or equal to m, Nr={sigma r1, sigma r2,..., sigma rn} is a regular neuron set, sigma ri is the i-th regular neuron set, and i is larger than or equal to 1 and smaller than or equal to n; the system represents the relation of connection between the proposition neuron and the regular neuron, but no relation of connection exists between two proposition neurons or between two regular neurons; I which is an element of a set Np is an input neuron set; O which is an element of the set Np is an output neuron set. According to the self-adapting fuzzy pulse neurolemma system provided by the invention, the fuzzy pulse neurolemma system can be reasoned by setting two types of neuron sets, and the system has learning capacity and can be used for handling with dynamic problems in application.

Description

technical field [0001] The invention relates to the field of automatic control, and more specifically relates to an adaptive fuzzy impulse nerve membrane system, a reasoning algorithm and a learning algorithm. Background technique [0002] The spiking neuromembrane system (also called spiking neuron P system) is a distributed parallel computing model, which is developed on the basis of membrane computing, and is a new model that combines the characteristics of spiking neurons with the idea of ​​membrane computing. [0003] The spiking neural membrane system is very practical due to its parallel processing capability, easy understanding, synchronization and nonlinearity. Therefore, in order to make full use of the advantages of the spiking neural membrane system, many improved spiking neural membrane systems have been proposed in recent years. Systems, such as the fuzzy pulse nerve membrane system used to deal with fuzzy knowledge expression and fuzzy reasoning in practical a...

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Problems solved by technology

[0004] However, in practical applications, most of the systems in process control, expert system, fault diagnosis and other applications are dynamic systems. They need to dynamically adjust their own parameters according to changes in the input data in the environment to adapt to changes in the environment. Due to the lack of such adaptive adjustment ability (that is, learning ability), the fuzzy pulse neural membrane system can only deal with the static problems in these applications, but cannot deal with the dynamic problems in the applications.

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