A time sequence prediction method and device based on an intuitive cyclic fuzzy neural network

Abstract

The invention discloses a time sequence prediction method and device based on an intuitive cyclic fuzzy neural network, and the method comprises the steps: receiving a time sequence data sample, and dividing the time sequence data sample into a plurality of groups of sub-samples; sequentially inputting the plurality of groups of sub-samples into an interval II type intuitive fuzzy inference systemfor fuzzy processing and learning prediction; after a group of samples are input into the system network, updating the system network by adopting a meta-cognitive learning algorithm according to thegenerated prediction error and the spherical situation; the interval II type intuitive fuzzy inference system comprises an input layer, a fuzzification layer, a space emission layer, a time trigger layer and an output layer. The fuzzification layer uses a TSK type fuzzification rule; wherein the spatial emission layer is used for obtaining the membership degree and the non-membership degree of input data, the spatial emission layer is used for carrying out dimension reduction processing on the membership degree and the non-membership degree, the time trigger layer is used for establishing a time relation for the data by using LSTM recursion, and the output layer is used for carrying out defuzzification processing based on a center to output the data.

Description

technical field [0001] The disclosure belongs to the technical field of data processing, and relates to a time series prediction method and device based on an intuitionistic circular fuzzy neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In 1965, Professor L.A.Zadeh of the University of California, Berkeley proposed an important concept to express the fuzziness of things: membership function, which is the well-known fuzzy set theory (Type-1Fuzzy Sets) in one or more embodiments of the present disclosure. Provides a foundation for dealing with information that is uncertain, ambiguous, and imprecise. In 1969, P.N.Marinos published a research report on fuzzy logic. In 1974, L.A. Zadeh published a research report on fuzzy reasoning. In the same year, E.H. Mamdani of the United Kingdom realized the world's first experimental ...

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Problems solved by technology

However, the type II fuzzy set has high computational complexity, which is difficult to meet the needs of practical engineering for computational efficiency. As a result, it has not been developed rapidly in the following two decades. People's research on fuzzy set theory is still based on a type fuzzy set

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