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Interval type II intuitionistic fuzzy random vector function connected neural network design method

An intuitive fuzzy, random vector technology, applied in the field of computational intelligence, can solve the problems of redundant rules in a complete rule base, falling into local minima, rule explosion, etc., to enhance the global search ability, increase representativeness and breadth, and flexible change. Effect

Pending Publication Date: 2018-12-11
HENAN UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

However, in the current research, the determination of the fuzzy rule base is completely determined manually based on expert experience, and the obtained complete rule base may contain redundant rules, and the problem of "rule explosion" will appear as the dimension of the input vector increases.
At the same time, the membership function and non-membership function of the antecedents of the fuzzy rules are basically changed from Gaussian functions, and the form is too simple; in the process of parameter optimization, the membership functions and non-membership functions of the antecedents of the rules both present strong nonlinear characteristics. It is easy to get stuck in local minima when using gradient descent; finally, the rule-based consequence uses a linear function of the input vector, and the ability to express non-linear functions is limited

Method used

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  • Interval type II intuitionistic fuzzy random vector function connected neural network design method
  • Interval type II intuitionistic fuzzy random vector function connected neural network design method
  • Interval type II intuitionistic fuzzy random vector function connected neural network design method

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Embodiment 1

[0054] Such as figure 1 Shown, a kind of interval two type intuitionistic fuzzy random vector function connection neural network design method of the present invention comprises the following steps:

[0055] Step S101: Construct interval-type II intuitionistic fuzzy random vector function connection neural network IT2IF-RVFLNN;

[0056] Step S102: Adjust the structure and parameters of IT2IF-RVFLNN by using a hybrid learning method combining quantum clustering algorithm, swarm intelligence algorithm and least square method.

[0057] Specifically, the IT2IF-RVFLNN has a seven-layer structure, wherein the first layer to the fourth layer perform the operation of the antecedent, and the fifth layer to the seventh layer perform the operation of the consequent.

[0058] Specifically, the step S101 includes:

[0059] Step S1011: using the membership function and non-membership function based on the β function as the antecedent of the interval-type II intuitionistic fuzzy rule of IT...

Embodiment 2

[0095] Such as figure 2 As shown, another interval two-type intuitionistic fuzzy random vector function connection neural network design method of the present invention includes:

[0096] Step S201: Construct an interval-type II intuitionistic fuzzy random vector function connection neural network IT2IF-RVFLNN.

[0097] Interval-2 type intuitionistic fuzzy reasoning is a logical reasoning method that uses interval-2 type intuitionistic fuzzy sets as antecedents of fuzzy rules (at least one of the antecedents is an interval-2 type intuitionistic fuzzy set), and has a definite explanatory ability. However, the traditional fuzzy reasoning technology lacks the learning ability of structure and parameters, so the present invention adopts neural network technology to make it have powerful learning ability. In addition, since the consequence of traditional interval-type intuitionistic fuzzy reasoning adopts interval-type intuitionistic fuzzy sets or linear functions of input vector...

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Abstract

The invention relates to the technical field of computational intelligence, in particular to an interval type II intuitionistic fuzzy random vector function connection neural network design method. The interval type II intuitionistic fuzzy random vector function connection neural network design method comprises the following steps: constructing an interval type II intuitionistic fuzzy random vector function connection neural network; adopting a hybrid learning method combining quantum clustering algorithm, swarm intelligence algorithm and least square method to adjust the structure and parameters of the neural network. The neural network adopts the membership function and the non-membership function based on the beta function as the forepart of the fuzzy rule, and adopts the random vectorfunction to connect the neural network as the afterpart of the interval fuzzy rule. The IT2IF-RVFLNN designed by the invention can achieve global approximation.

Description

technical field [0001] The invention relates to the technical field of computational intelligence, in particular to a design method for a neural network connection of interval-type two intuitionistic fuzzy random vector functions. Background technique [0002] In 1965, Zadeh proposed fuzzy set theory, which successfully solved the defect that traditional set theory could not describe uncertain information reasonably. However, primitive fuzzy sets cannot describe the degree of hesitation with which elements belong to sets (attributes, characteristics). Aiming at this problem, Atanassov proposed the concept of intuitionistic fuzzy set, and introduced hesitation function to describe the unknowable property of this membership relationship. On this basis, Glad Deschrijver, Petr Hájek and others studied the triangular norm and anti-triangular norm of intuitionistic fuzzy sets, intuitionistic fuzzy reasoning and hierarchical direct fuzzy reasoning, and applied them to real-time tr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06N3/04
CPCG06N3/006G06N3/04
Inventor 赵亮董维中谢志峰赵自广
Owner HENAN UNIVERSITY OF TECHNOLOGY
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