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Distributed principal component analysis neural network modeling method for chemical exothermic reaction

A neural network modeling and exothermic reaction technology, which is applied in the field of distributed principal element analysis neural network modeling of chemical exothermic reactions, and can solve problems such as the difficulty of modeling the catalytic rod object.

Active Publication Date: 2016-07-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the modeling process of the catalytic rod object in the chemical reaction is relatively difficult. By means of data collection, model establishment and parameter optimization, a principal component analysis algorithm and a genetic algorithm are proposed to optimize the radial Modeling Method of Nonlinear Distributed Parameter System Based on Neural Network

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  • Distributed principal component analysis neural network modeling method for chemical exothermic reaction
  • Distributed principal component analysis neural network modeling method for chemical exothermic reaction
  • Distributed principal component analysis neural network modeling method for chemical exothermic reaction

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Embodiment Construction

[0070] The present invention will be further described below in conjunction with embodiment.

[0071] Take the catalytic rod as the object of the actual process.

[0072] Step 1. Collect the real-time operation data of the catalytic rod process, and establish the distributed parameter model of the catalytic rod object.

[0073] 1.1 to The spatiotemporal data entered for the catalytic rod, The output data collected for the catalytic rod, and the corresponding catalytic rod state variables where t is the time series, L is the length of the time series, z i is the spatial position of the collected i-th group of catalytic rod output data, and N is the total number of collected output data.

[0074] 1.2 The space-time variable X(z,t) in the catalytic rod can be obtained by Fourier transform:

[0075]

[0076] According to the actual situation, it can be converted into a finite space:

[0077]

[0078] in is an approximation of n times, is the orthogonal basis fun...

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Abstract

The invention discloses a distributed principal component analysis neural network modeling method for a chemical exothermic reaction. According to the method, input and output data of an object is collected, a principal component analysis method is utilized for dividing a distributed parameter system into an autoregressive linear model and a non-linear model by reducing dimensionality, and the autoregressive linear model is identified through a least square method. For the non-linear model, the least square method is utilized for establishing an RBF neural network model of the object, and then parameters of the RBF neural network model are optimized through a genetic algorithm. The established models have high precision, and dynamic properties of the process object can be well described.

Description

technical field [0001] The invention belongs to the technical field of industrial automation and relates to a distributed principal component analysis neural network modeling method for chemical exothermic reactions. Background technique [0002] In the actual industrial chemical reaction process, the chemical reaction phenomenon of heat flow is a nonlinear partial differential method, and its input and output variables are constantly changing with space and time. This type of system is called a distributed parameter system. (DPS). Traditional spatial discretization methods, such as finite difference methods, usually approximate the system as a high-order ordinary differential equation, which is not conducive to real-time control and cannot accurately reflect the internal model structure of the system. Principal component analysis (PCA) is based on the original data space, by constructing a new set of latent variables to reduce the dimensionality of the original space, and ...

Claims

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Application Information

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IPC IPC(8): G06F17/10G06N3/08G06N3/12
CPCG06F17/10G06N3/086G06N3/123
Inventor 张日东徐卫德陶吉利
Owner HANGZHOU DIANZI UNIV
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