Industrial process modeling forecasting method oriented at flow object
Industrial process modeling forecasting method oriented at flow object
 CN 104,732,067 A
 Filed: 02/26/2015
 Published: 06/24/2015
 Est. Priority Date: 02/26/2015
 Status: Active Application
First Claim
1. an industrial process modeling Forecasting Methodology for ProcessOriented object, is characterized in that comprising the steps:
 (1) FNT model is set up, and extracts industrial flow object raw data set S from the data warehouse that flow object has generated, and create the initial population of FNT model, population at individual number customizes as required, and each individuality represents a FNT model;
(2) utilize PIPE algorithm optimization FNT model structure, adaptive value function adopts square error or rootmeansquare error;
(3) Particle Swarm (PSO) algorithm optimization FNT model parameter is utilized;
(4) FNT model is utilized to carry out modeling and forecasting to flow object production run.
Abstract
The invention discloses an industrial process modeling forecasting method oriented at a flow object. The method comprises the following steps: building FNT models, extracting an industrial flow object original data set S from a data warehouse which has already been generated by the flow object, creating an initial species group of the FNT models, and customizing the individual numbers of the species group as required, wherein each individual represents an FNT model; utilizing the PIPE algorithm for optimizing FNT model structures, and adopting mean square errors or rootmeansquare errors for fitness functions; utilizing the particle swarm optimization (PSO) algorithm for optimizing FNT model parameters; utilizing the FNT models for conducting modeling forecast for a flow object production process. According to the method, based on the flexible neural tree, an equation of variation tendency among measuring point data of the flow object is obtained, the industrial production process is simulated, based on relevant parameters of a current production state, production states in a period of time in the future are forecast, so that an enterprise is assisted and instructed for adjusting the production process parameters, and the production is guided for drawing on advantages and avoiding disadvantages in a microcosmic sense.

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8 Claims

1. an industrial process modeling Forecasting Methodology for ProcessOriented object, is characterized in that comprising the steps:

(1) FNT model is set up, and extracts industrial flow object raw data set S from the data warehouse that flow object has generated, and create the initial population of FNT model, population at individual number customizes as required, and each individuality represents a FNT model; (2) utilize PIPE algorithm optimization FNT model structure, adaptive value function adopts square error or rootmeansquare error; (3) Particle Swarm (PSO) algorithm optimization FNT model parameter is utilized; (4) FNT model is utilized to carry out modeling and forecasting to flow object production run.


2. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1, it is characterized in that:
 in described step (1) industrial flow object raw data set S, each attribute or field represent the state of certain node in industrial flow object, its value can change along with the change of the change of time and other node states, the data of FNT model treatment need [0, 1] between, so need to be normalized raw data set S, method for normalizing is as formula (1), wherein X is pending raw data, MAX and MIN is the minimum and maximum value of data attribute in raw data set S belonging to X respectively,
Y＝
(XMIN)/(MAXMIN) (1)Then raw data set S later for normalization is loaded into database, forms the data warehouse that can be directly used in data mining.
 in described step (1) industrial flow object raw data set S, each attribute or field represent the state of certain node in industrial flow object, its value can change along with the change of the change of time and other node states, the data of FNT model treatment need [0, 1] between, so need to be normalized raw data set S, method for normalizing is as formula (1), wherein X is pending raw data, MAX and MIN is the minimum and maximum value of data attribute in raw data set S belonging to X respectively,

3. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1 and 2, is characterized in that:
 the collection of functions F that described FNT model is used and termination message collection T is described below;
S＝
F∪
T＝
{+ _{2},+ _{3},...,+ _{N}}∪
{x _{1},...,x _{n}} (2)Wherein ,+ _{i}represent nonleaf nodes information, i=2,3 ..., N, i representative function+ _{i}the number of corresponding input variable;
x _{1}, x _{2}... x _{n}for leaf node information;
The output of a nonleaf nodes is regarded as a flexible neuron calculate, namely+ _{i}it is the flexible neuron with i input;
In the constructive process of Neural Tree, if nonterminal information+ _{i}selected, the actual value of i is random generation, its expression+ _{i}connection weights between this node and his child'"'"'s contact;
Two adjustable parameter a of flexible actuation function _{i}and b _{i}also be random generation;
The excitation function of FNT;
Be expressed as;
 the collection of functions F that described FNT model is used and termination message collection T is described below;

4. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1, it is characterized in that:
 the search volume of described step (2) PIPE algorithm is the treelike population produced according to the raw data set S preset, individuality results from the probability vector space covering institute'"'"'s likely individuality;
Individual with the generation of probability prototype tree, expression is the tree construction of a n dimension, the maximum branch number that information in n representative function collection can produce, the nonleaf nodes of tree results from collection of functions F, leaf node results from termination message collection T, the number of the subtree of each node is decided by the function information of each node producible point of number, and the input of each branch has corresponding subtree to calculate, the analysis mode of tree be depthfirst from left to right.
 the search volume of described step (2) PIPE algorithm is the treelike population produced according to the raw data set S preset, individuality results from the probability vector space covering institute'"'"'s likely individuality;

5. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1 or 4, is characterized in that:
 described step (2) PIPE algorithm flow comprises;
(21) individual generation, produces individual with probability prototype tree, represent body one by one, wherein 0<
j<
=PS, PS represent the scale that per generation is individual;
(22) individual evaluation, each population at individual all to evaluate in given problem, and according to predefined adaptive value function formula, as shown in formula (5) and (6), calculate adaptive value the best individuality (individuality that adaptive value is minimum) of current population is marked as program performs till now, and best individuality is stored in in, Fit (i) represents ith individual adaptive value, and p represents number of samples, y _{1}^{j}and y _{2}^{j}represent the actual sequence value of a jth sample and a jth sample final output valve through ith individuality calculating respectively,
 described step (2) PIPE algorithm flow comprises;

6. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1, it is characterized in that:
 the mathematical description of described step (3) particle swarm optimization is as follows;
set particle populations scale as N, wherein the coordinate position vector representation of each particulate in D dimension space is velocity vector is expressed as particulate personal best particle, namely the optimal location that lives through of this particulate, is designated as colony'"'"'s optimal location, the optimal location that namely in this Particle Swarm, any individual lives through, is designated as${\stackrel{\→g=({p}_{g1},{p}_{g2},CenterDot;CenterDot;CenterDot;,{p}_{\mathrm{gd}},CenterDot;CenterDot;CenterDot;,{p}_{\mathrm{gD}});}{P}}_{}$ The iterative formula of personal best particle is;
 the mathematical description of described step (3) particle swarm optimization is as follows;

7. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 6, is characterized in that:
 after described step (3) parameter optimization, FNT interior joint+ _{n}output be calculated as follows;
 after described step (3) parameter optimization, FNT interior joint+ _{n}output be calculated as follows;

8. the industrial process modeling Forecasting Methodology of a kind of ProcessOriented object according to claim 1, it is characterized in that:
 flow object critical workflow and corresponding production input and output parameter are input in step (2) and the revised FNT model of step (3) by described step (4), obtain the output function of shape as formula 16, wherein A _{n}and B _{n}the parameter after two groups of PSO optimize, net _{n}it is the tree structure after PIPE optimizes;
This function is exactly the Changing Pattern of production procedure parameter, carries out detailed predicting for the change of producing future;
 flow object critical workflow and corresponding production input and output parameter are input in step (2) and the revised FNT model of step (3) by described step (4), obtain the output function of shape as formula 16, wherein A _{n}and B _{n}the parameter after two groups of PSO optimize, net _{n}it is the tree structure after PIPE optimizes;
Specification(s)