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Cam profile fitting method based on RBF neural network

A neural network and cam-type technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as unstable least squares fitting algorithm, large data errors, and large amount of calculations

Inactive Publication Date: 2015-07-08
XUCHANG UNIV
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Problems solved by technology

Due to the manufacturing error of the cam, wear and measurement errors during use, etc., the actual measured discrete point data deviates greatly from the original theoretical value. If the discrete point distance is relatively large, the measured data error will be even greater.
However, the cubic spline interpolation and least squares fitting methods have disadvantages such as algorithm instability and large amount of calculation.

Method used

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  • Cam profile fitting method based on RBF neural network
  • Cam profile fitting method based on RBF neural network
  • Cam profile fitting method based on RBF neural network

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Embodiment Construction

[0020] The present invention proposes a kind of cam profile fitting method based on RBF neural network, comprises the following steps:

[0021] Step 1: Establish the RBF model: The RBF neural network has input layer, intermediate hidden layer, and output layer nodes, and there is no coupling among nodes in the same layer. The input signal is input from the input layer, passes through each hidden layer node, and then is transmitted to the output node. The RBF network can be regarded as a highly nonlinear mapping from input to output, namely f(x):R n →R m ,. For a sample set: enter x i (∈R n ) and output y i (∈R m ), it can be considered that there exists a mapping g such that g(x i )=y i , i=1,2,...,n, it is required that a map f is the best approximation of g. After several times of compounding, complex functions can be approximated.

[0022] Step 2: The improved learning algorithm of the RBF model: use the subtractive clustering algorithm to cluster the learning sam...

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Abstract

The invention discloses a cam profile fitting method based on the RBF neural network. The method comprises the steps of establishing an RBF neural network model, conducting subtractive clustering learning on the RBF model, training the RBF model, and conducting model error index evaluation. According to the cam profile fitting method based on the RBF neural network, the number of hidden layer neuron nodes can be determined conveniently and quickly by means of the subtractive clustering algorithm, and the number of times of iterations of the algorithm is effectively reduced. Compared with the BP neural network, the RBF network has the advantages that calculation amount is small, calculation speed is high, generalization ability is high, robustness is high, and the RBF network has high application value in the field of cam profile fitting and other mechanical part surface shape fitting fields.

Description

technical field [0001] The invention relates to the technical field of a cam profile fitting method, in particular to the technical field of a cam profile fitting method based on an RBF neural network. Background technique [0002] Cam is an important transmission part in some mechanical systems, and its operation will directly affect the work of the whole machine or unit. The accuracy of the cam profile is often an important indicator of the quality of the machine. [0003] When people perform high-precision detection of the linear error of the cam, they need to know the theoretical lift value corresponding to any angular position of the cam. The actual situation is that some cam manuals only give the theoretical lift value of the cam with a larger angular spacing, but do not provide the theoretical value of the smaller spacing, which is convenient for high-precision measurement of the cam. [0004] There are two methods commonly used to fit the curve equation from the ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 胡万强
Owner XUCHANG UNIV
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