KP model density function identification method based on self-adaption bat search algorithm

A technology of bat search and density function, applied in the field of identification, can solve problems such as large amount of calculation and limited identification accuracy.

Active Publication Date: 2016-12-07
JILIN UNIV
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Problems solved by technology

The maximum likelihood parameter estimation method is widely used because of its good asymptotic properties,

Method used

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  • KP model density function identification method based on self-adaption bat search algorithm
  • KP model density function identification method based on self-adaption bat search algorithm
  • KP model density function identification method based on self-adaption bat search algorithm

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[0078] Step 1: First use the figure 1 The input and output data of the piezoelectric ceramic actuator shown, and then the initial position of the bat individual corresponding to the parameters to be identified is randomly generated by the adaptive bat search algorithm , and determine the population size of the bat population as , the number of density functions to be identified , the pulse firing rate , pulse volume . According to the objective function ,in, is the number of data used to find the best fitness value at the initial moment (that is, the minimum value of the objective function at this time) and the current best position .

[0079] Step 2: Use formula (1), formula (2) and formula (3) to update the flight search speed and position of individual bats, where The firing frequency of the bat is used to adjust the flying search speed of the bat, ; is a random vector subject to uniform distribution; is the speed update inertia weight coefficient o...

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Abstract

The invention discloses a KP model density function identification method based on the self-adaption bat search algorithm and belongs to the technical field of identification. The KP model density function identification method based on the self-adaption bat search algorithm aims to introduce the self-adaption concept into the bat search algorithm to enable a density function selected by the bat search algorithm during flight searching to change in a self-adaption mode according to the algorithm, so that global convergence is improved. According to the method, an objective function is set first, the position of a bat individual corresponding to a parameter to be identified is generated randomly by the self-adaption bat search algorithm, updating is conducted by means of a position and speed updating formula to generate a new bat position, whether a random number is met is judged, the fitness function value of each bat individual is calculated, a group is ranked according to fitness values, and whether the maximum iteration number is met is judged. By the adoption of the method, defects of the prior art are overcome, and the optimal KP model density function combination is identified by means of the self-adaption bat search algorithm.

Description

technical field [0001] The invention belongs to the technical field of identification. Background technique [0002] At present, the identification of the KP model density function mainly includes the following methods: [0003] The least squares method is the most widely used model identification method, which determines the parameters of the system model by minimizing the sum of squares function of the generalized error. The least squares method is widely used in parameter estimation and model identification due to its concise principle, fast convergence, easy understanding, and easy programming. However, the least squares method is inconsistent and biased. Therefore, in order to overcome its shortcomings, a A series of improved methods: the batch method of least squares, the recursive method of least squares, the recursive method of forgetting factor of least squares and the recursive extension method. The improved least squares method is generally suitable for the iden...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 周淼磊徐瑞高巍王晨阳刘月罗祎灵
Owner JILIN UNIV
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