CN114492569A - Typhoon path classification method based on width learning system - Google Patents

Typhoon path classification method based on width learning system Download PDF

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CN114492569A
CN114492569A CN202111565583.9A CN202111565583A CN114492569A CN 114492569 A CN114492569 A CN 114492569A CN 202111565583 A CN202111565583 A CN 202111565583A CN 114492569 A CN114492569 A CN 114492569A
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贺治国
朱业
马赫
卢美
季余
韩东睿
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Zhejiang Marine Monitoring And Prediction Center
Zhejiang University ZJU
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Abstract

The invention discloses a typhoon path classification method based on a width learning system. The invention specifically comprises the following steps: s1, performing cluster analysis on the typhoon path by using the existing typhoon path data through an improved DBSCAN algorithm, and establishing a category label matrix of the typhoon path; s2, performing feature representation on the typhoon paths by using an improved hierarchical clustering algorithm, and unifying dimensions of all the typhoon paths; and S3, constructing a typhoon path classification model based on the width learning system, taking the type label matrix for establishing the typhoon path established in S1 and the typhoon path feature matrix in S2 as input samples, and calculating a weight matrix from system input to output so as to realize the classification of the typhoon paths. The method of the invention adopts the width learning system to classify the typhoon paths, thereby realizing the automatic classification of the typhoon paths.

Description

Typhoon path classification method based on width learning system
Technical Field
The invention belongs to the technical field of typhoon path classification, and particularly relates to a typhoon path classification method based on a width learning system.
Background
Typhoon is one of the most serious natural disasters in the world, so that the research on the change rule and the cause of typhoon activity has great scientific significance on typhoon forecasting, disaster prevention and reduction.
The width learning system (BLS) is an incremental learning algorithm based on a random vector function link neural network, and is more suitable for processing work requiring larger calculation amount, such as data classification and the like, compared with a common neural network because the training process does not need to repeatedly iterate sample data and the weight matrix of a network output layer is calculated in a mode of solving the pseudo-inverse through ridge regression.
The width learning system is composed of a feature mapping layer, an enhanced node layer and an output layer, wherein the feature mapping layer and the enhanced vector layer are used as the input of the system together. And the feature mapping layer randomly generates weights through a feature mapping function to realize the feature extraction of the sample. The enhancement node layer performs enhancement calculation on the feature vectors through orthogonal normalized random weights, and introduces the nonlinear classification capability of the activation function enhancement model, so that the purpose of fully extracting the feature information of the sample data is achieved. And finally, performing pseudo-inverse operation on the synthetic matrix of the feature mapping layer and the enhanced node layer to obtain a weight matrix from input to output of the system. When the width learning system is used for solving the classification problem, the data set to be classified and the label set of the data set to be classified are used as input of the width learning system, the width learning system is trained, the width learning system outputs the probability that each data belongs to each category respectively, and the category with the maximum probability is the category which is judged by the data most.
The typhoon path is a time sequence formed by the positions where the typhoon center points pass in the movement process of the typhoon, and the movement path is complex due to the influence of regional environment, climate and the like on the typhoon movement, so that the typhoon path is difficult to classify and analyze. At present, the classification research on the typhoon paths mainly focuses on clustering analysis on the typhoons, and the typhoon paths are classified by using a similarity threshold. However, the method is only suitable for fewer typhoon path classifications, and when the typhoon path set is large, the definition of the similarity threshold is difficult, and the generality is poor.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a typhoon path classification method using a width learning system in combination with typhoon path feature representation.
The method comprises the following steps:
and S1, constructing a typhoon path category set. Clustering analysis is carried out on typhoon paths by using an improved DBSCAN algorithm, the similarity among the paths is calculated by using a dynamic time warping algorithm instead of a similarity calculation method in the DBSCAN algorithm, the paths with large similarity are automatically gathered into one category, and a category label matrix of the typhoon paths is established;
and S2, constructing a feature matrix of the typhoon path. The improved hierarchical clustering algorithm is utilized to unify the length of the typhoon paths through characteristic representation, and as the typhoon path points are data sequences which are strictly arranged according to the time sequence, the hierarchical clustering algorithm is improved, only hierarchical clustering analysis is carried out on adjacent points of each path, the Euclidean distance (Euclidean distance) of the adjacent points is taken as the measurement standard, the points with smaller Euclidean distance are merged, all the typhoon paths are unified in length, the characteristic representation of each typhoon path is realized, and a typhoon path characteristic matrix is obtained;
and S3, establishing a typhoon path multi-classification model based on the width learning system, and classifying the typhoon paths by using the model. The invention relates to a width learning system (BLS) which is an incremental learning algorithm based on a random vector function link neural network, wherein a typhoon path type label matrix obtained at S1 and a typhoon path feature matrix obtained at S2 are used as the input of a model, a feature mapping layer randomly generates weights to perform feature extraction on input samples, an enhanced node layer performs enhanced calculation on feature vectors through orthogonal normalized random weights, the nonlinear classification capability of the model is enhanced by using an activation function, finally, a pseudo-inverse operation is performed on a composite matrix of the feature mapping layer and the enhanced node layer to obtain a weight matrix from the input to the output of the system, the output value of each row of the output matrix is the probability that each path obtained by the BLS belongs to each row of categories, and the position index of the maximum value is the category distinguished by each test path, so that the classified prediction of the typhoon paths is realized.
Compared with the prior art, the invention has the following advantages:
firstly, the method of the invention adopts an improved DBSCAN clustering algorithm to establish the category set of the typhoon paths, and the category set can be automatically generated according to the similarity of each historical typhoon path.
Secondly, the method adopts a characteristic representation method of improved hierarchical clustering, unifies the scales of different typhoon path data, and provides a basis for classification.
Thirdly, the method of the invention adopts a width learning system to classify the typhoon paths, thus realizing the automatic classification of the typhoon paths.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a typhoon path classification method based on a width learning system according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the present invention comprises the steps of:
and S1, establishing a typhoon path category set through a clustering algorithm.
The typhoon path is a track sequence in which typhoon centers are arranged according to time sequence in the typhoon motion process, and the DBSCAN algorithm is a clustering algorithm based on density, can find clusters in any shape, automatically determines the number of the clusters, and has robustness on noise, so that the method is very suitable for carrying out cluster analysis on track data of the typhoon path, and automatically clustering paths with high similarity into one type. Similarity is generally measured by distance, and the smaller the distance, the greater the similarity. When the original DBSCAN algorithm is used for cluster analysis, an Euclidean distance measurement method is adopted, the method is only suitable for typhoon paths with the same number of points, and the fluctuation condition of the paths is not considered, so that the dynamic time warping algorithm is used as the distance measurement method of the paths. The dynamic time warping algorithm is to extend or shorten different paths to be compared until the lengths are consistent, and then calculate the distance between the paths. Therefore, the typhoon path category set establishment process is as follows:
(1) setting a scanning neighborhood radius eps, wherein the minimum number of paths minPts is included, starting from an unvisited path in an optional typhoon path sample, calculating the distance between the path and other paths by using a dynamic time warping algorithm, and finding out all nearby paths which are within the eps (including the eps) from the path.
(2) If the number of nearby paths is equal to or greater than minPts, the current path forms a cluster with its nearby paths, and the departure path is marked as visited.
(3) Repeating steps (1) and (2), processing all paths which are not marked as visited in the cluster, and if the number of nearby paths is less than minPts, marking the paths as noise data.
(4) If all paths in the cluster are marked as accessed, repeating the steps (2) to (3) until all objects are classified into a certain cluster or marked as noise data, and outputting the category label matrix of each path. The feature matrix of each path is denoted as the label matrix of the path features as Y.
At S2, the data length of each typhoon path may be different, and therefore, the classification cannot be performed by directly constructing the feature matrix using the raw data. In consideration of the fact that the typhoon paths cannot be accurately fitted by using a functional relation, the same number of points are extracted from each typhoon path by using an improved hierarchical clustering algorithm to represent the typhoon paths, the process is named as typhoon path feature representation, each extracted point is called a feature point, and each feature point forms a feature path. The calculation principle of the hierarchical clustering algorithm is to calculate the distance between samples and combine the points with the closest distance into the same class each time. Then, the distance between the classes is calculated, the classes with the closest distance are combined into a large class, and the large class is combined continuously until a specified condition is reached. Book (I)The invention utilizes an improved hierarchical clustering method to perform characteristic representation on the typhoon path, combines points with small Euclidean distance by only taking the Euclidean distance (Euclidean distance) of adjacent points as a measurement standard, and takes the combined points as characteristic points of the path. If liIs a point TiTo point Ti+1The distance of (a) to (b),
Figure BDA0003421892150000031
wherein xiAnd yiAre respectively a point TiLongitude and latitude, xi+1And yi+1Are respectively a point Ti+1The longitude and latitude of (a) are specifically calculated as follows:
a1, calculating the distance l between each adjacent point on the original typhoon pathi。
A2, dividing all distances liStored in set S, S ═ { l }1,l2,…,ln-1In which l1Is a point T1And point T2Distance between points,/2Is a point T2And point T3Distance between ln-1Is a point Tn-1And point TnI denotes the number of dots and n denotes the total number of dots.
And A3, merging the two points with the minimum distance in the S set into one point, and representing the point by the mean value of the longitude and the latitude of the two points to form a new path.
A4, recalculating the distance between each adjacent point on the new path according to step A2, and updating the set S.
And A5, turning to the step A3 until the point number of all new paths reaches the length of the shortest path set in the sample set, and outputting a characteristic path set F.
And S3, establishing a typhoon path classification model based on the width learning system, and performing classification prediction based on the model. The method comprises the steps of taking a characteristic path set F and a label matrix Y as input, randomly generating weights by a characteristic mapping layer to carry out characteristic extraction on input samples, carrying out enhanced calculation on characteristic vectors by an enhanced node layer through orthogonal normalized random weights, enhancing the nonlinear classification capability of a model by using an activation function, finally carrying out pseudo-inverse operation on a synthetic matrix of the characteristic mapping layer and the enhanced node layer to obtain a weight matrix input to output by the system, wherein the output value of each row of the output matrix is the probability that each path obtained by the BLS belongs to each category, and the position index of each row of the maximum value is the category judged by each test path, so that the classified prediction of typhoon paths is realized.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. A typhoon path classification method based on a width learning system is characterized by comprising the following steps:
s1, constructing a typhoon path category set;
the improved DBSCAN algorithm is used for carrying out cluster analysis on the typhoon path, and specifically comprises the following steps: calculating the similarity between paths by using a dynamic time warping algorithm instead of a similarity calculation method in a DBSCAN algorithm, and automatically grouping paths with large similarity into one category; establishing a category label matrix of the typhoon path;
s2, constructing a feature matrix of the typhoon path;
the method utilizes an improved hierarchical clustering algorithm to unify the length of the typhoon path through feature representation, and specifically comprises the following steps: improving a hierarchical clustering algorithm, only performing hierarchical clustering analysis on adjacent points of each path, combining the points with smaller Euclidean distance by taking the Euclidean distance of the adjacent points as a measurement standard, unifying the lengths of all typhoon paths, and realizing the characteristic representation of each typhoon path; obtaining a typhoon path feature matrix;
s3, typhoon path classification is carried out by using a typhoon path multi-classification model based on a width learning system;
taking the typhoon path type label matrix obtained in the step S1 and the typhoon path feature matrix obtained in the step S2 as the input of the model;
randomly generating weights by a feature mapping layer in the typhoon path multi-classification model to perform feature extraction on input samples, performing enhanced calculation on feature vectors by an enhanced node layer through orthogonal normalized random weights, and enhancing the nonlinear classification capability of the model by using an activation function;
and finally, performing pseudo-inverse operation on a synthetic matrix of the feature mapping layer and the enhanced node layer to obtain a weight matrix from system input to output, wherein the output value of each row of the output matrix is the probability that each path obtained by the width learning system belongs to each category, and the position index of the maximum value of each row is the category judged by each test path, so that the classified prediction of the typhoon paths is realized.
2. The method for classifying typhoon paths based on the width learning system as claimed in claim 1, wherein the constructing step of the typhoon path class label matrix in S1 is:
s11, setting a scanning neighborhood radius eps, wherein the scanning neighborhood radius eps comprises the number minPts of paths at minimum, starting with an unaccessed path in an optional typhoon path sample, calculating the distance between the path and other paths by using a dynamic time warping algorithm, and finding out all nearby paths which are within the eps of the path;
s12, if the number of the nearby paths is more than or equal to minPts, the current path and the nearby paths form a cluster, and the departure path is marked as visited;
s13, repeating S11-S12, processing all paths which are not marked as visited in the cluster, and if the number of nearby paths is less than minPts, marking the paths as noise data;
and S14, if all paths in the cluster are marked as accessed, repeating the steps S12-S13 until all objects are classified into a certain cluster or marked as noise data, and outputting the category label matrix of each path.
3. The typhoon path classification method based on the width learning system as claimed in claim 1, wherein the typhoon path feature matrix constructing step in S2 is:
s21, calculating the distance l between each adjacent point on each original typhoon pathi；
S22, dividing all distances liStored in the set S; s ═ l1,l2,…,ln-1In which l1Is a point T1And point T2Distance between points, l2Is a point T2And point T3Distance between ln-1Is a point Tn-1And point TnI represents the serial number of the points, and n represents the total number of the points;
s23, combining two points with the minimum distance in the set S into one point, and representing the point by the mean value of the longitude and the latitude of the two points to form a new path;
s24, recalculating the distance between the points adjacent to the newly generated point of the new path according to the step S22, and updating the set S;
and S25, turning to the step S23, and outputting the feature path set until the point number of all the new paths reaches the length of the shortest path set in the sample set.
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