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Multi-objective Genetic Algorithm and rbf Neural Network Optimization Modeling Method for Coking Oven Pressure

A multi-objective genetic and neural network model technology, applied in the field of multi-objective genetic algorithm and RBF neural network optimization modeling, can solve the problems of difficult modeling process of coking furnace furnace pressure object, and achieve high accuracy and good dynamic characteristics Effect

Active Publication Date: 2018-08-21
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 coking furnace furnace pressure object is relatively difficult, and provide a multi-objective genetic algorithm and RBF neural network structure for the coking furnace furnace pressure through data collection, model establishment, optimization and other means Parameter Optimization Modeling Method

Method used

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  • Multi-objective Genetic Algorithm and rbf Neural Network Optimization Modeling Method for Coking Oven Pressure
  • Multi-objective Genetic Algorithm and rbf Neural Network Optimization Modeling Method for Coking Oven Pressure
  • Multi-objective Genetic Algorithm and rbf Neural Network Optimization Modeling Method for Coking Oven Pressure

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Embodiment Construction

[0032] Taking the coking furnace pressure as the actual object, taking the opening of the flue baffle as the input, and taking the coking furnace pressure as the output, the model of the coking furnace pressure is established.

[0033] The steps of the inventive method comprise:

[0034] Step 1. Collect the real-time operation data of the process and establish the RBF model of the process object. The specific steps are as follows:

[0035] 1.1 From the RBF neural network structure including the input layer, output layer and hidden layer, the mapping relationship of the network is obtained, that is, the input and output model of the system, in the following form:

[0036]

[0037] Among them, x=(x 1 ,x 2 ,...,x n ) represents the n input node vector, y represents the output variable of the network, c i ∈ R n Represents the center vector of the i-th hidden layer neuron, R n is the Euclidean space, is a Gaussian function, ||x-c i || means x to c i The radial distance...

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Abstract

The invention discloses a multi-objective evolutionary algorithm (MOEA) and radial basis function (RBF) neural network optimization modeling method of coking furnace pressure. According to the method, the input / output data of acquisition process objects is combined with an RBF neural network model, and a network layer and parameters of an improved MOEA optimization neural network are used. The method is higher in accuracy and is capable of well describing the dynamic characteristics of the process objects.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to a multi-objective genetic algorithm and RBF neural network optimization modeling method for coking oven pressure. Background technique [0002] In the actual industrial process, because the physical or chemical properties of many complex actual process objects are unknown, system modeling is a very important part of advanced control technology. For the dynamic characteristics of coking heating furnace furnace pressure, RBF neural network has a good approximation speed, can improve the accuracy of the pressure prediction model, and can simplify the model structure. Based on the actual process, a new radial basis function (RBF) neural network is proposed to improve the accuracy of the model and simplify its structure. Multi-objective genetic algorithm (MOEA) is an iterative adaptive stochastic global optimization search algorithm based on natural selection and natural genetics, wh...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50G06N3/02G06N3/12
CPCG06F30/00G06N3/02G06N3/126
Inventor 张日东王玉中
Owner HANGZHOU DIANZI UNIV
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