10 results about "Affinity propagation" patented technology

The invention provides a personalized federal learning method and system for multi-device heterogeneous data, and the method comprises the steps: transmitting a neural network structure to all clients, so as to enable all clients to carry out local model training; receiving the trained model parameters of each client, and dividing the trained model parameters of each client into non-BN layer parameters and BN layer parameters; all the non-BN layer parameters are aggregated; calculating distribution similarity among the clients according to all the BN layer parameters to obtain a similarity matrix; clustering the clients according to the similarity matrix by adopting an affinity propagation algorithm to obtain a client group with similar feature distribution; performing intra-group aggregation on the BN layer parameters corresponding to each group of clients; sending the aggregated non-BN layer parameters to all the clients, and sending the aggregated BN layer parameters in the groups to the clients in the corresponding groups; therefore, the training stability and the prediction precision in a multi-device heterogeneous environment are improved.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

A system and a method for updating a Machine Learning (ML) model are described The method involves capturing reconstruction errors associated with reconstruction of images by a pre-trained autoencoder. Data points representing the reconstruction errors are clustered using affinity propagation. A preference value used by the affinity propagation for determining similarity between the data points is dynamically set through linear regression. Outliers and data drifts are determined from clusters of the data points. Classification output of the ML model is associated with the outliers and the data drift, for refinement of the ML model over a device hosting a training environment.

The invention provides a time series data prediction method and device based on dynamic error modeling, and the method comprises the steps: predicting original time series data through an LSTM model, and generating a historical error sequence of a predicted value and a real value; carrying out CEEMDAN mode decomposition on the sequence, and extracting a plurality of error mode components with non-stationary characteristics; clustering the fusion components by using an affinity propagation algorithm to generate a representative error modal cluster after dimension reduction; analyzing the nonlinear correlation between the historical error sequence and each modal cluster based on a Spearman rank correlation coefficient, and obtaining a dynamic weight distribution result through normalization; and respectively predicting a future error value of each modal cluster through the parallel LSTM network, weighting and correcting a prediction result according to a dynamic weight, and generating a final prediction value. According to the time series data prediction method based on dynamic error modeling, parallel collaborative optimization of prediction and error correction is realized through explicit modeling error dynamic characteristics in combination with a CEEMDAN-AP joint optimization algorithm and Spearman correlation weight distribution, and the precision and real-time performance of time series prediction are remarkably improved.

The application discloses a visual detection method for early warning of icing environment, and relates to the technical field of aviation icing detection, comprising the following steps: constructing a two-dimensional joint feature residual space based on real-time contour vectors and real-time gray field vectors, generating a candidate region set, constructing a three-dimensional feature vector for maximum points in the candidate region set, clustering by using an affinity propagation clustering algorithm, and obtaining structured ice phase units; combining the affinity propagation clustering algorithm with an effective rank quantization method based on singular value entropy of a trajectory matrix to construct an ice layer analysis mechanism with multiple singular structure decoupling capabilities and continuous morphological phase state quantization capabilities; and constructing a spatial distribution sequence of internal deformation variables of each ice phase unit into a trajectory matrix, introducing singular value decomposition and Shannon entropy calculation to obtain an effective rank continuous scalar, and improving the perception granularity of the icing visual detection system on the spatial non-uniformity of the ice layer.

The application discloses a kind of comprehensive energy load aggregate short-term probability prediction model establishing method and device.The method includes: the historical data of comprehensive energy load aggregate collected is preprocessed and dimensionality reduction;Grouping is carried out to the data after dimensionality reduction using affinity propagation clustering algorithm based on comprehensive similarity, and the centroid feature of each group is extracted as representative load feature input prediction model;The short-term probability prediction model of comprehensive energy load aggregate that fusion Copula self-adapting correlation analysis, cross feature-time graph neural network and mixed density network is constructed.The precision of short-term probability prediction of comprehensive energy load aggregate is effectively improved by dynamic coupling relationship mining and spatiotemporal feature joint learning mechanism.

The invention relates to the technical field of data analysis, and provides a risk early warning method and device, electronic equipment and a program product, and the method comprises the steps: obtaining a data set, related to a target event, of a target object, and carrying out the classification of the target object through employing the data set based on an affinity propagation algorithm, and obtaining a plurality of target sub-objects; based on the feature combination and the weight vector of each target sub-object in different time periods in the monitoring period, constructing an adaptive iterative risk assessment model, and determining the risk value information of each target sub-object in each time period according to the risk assessment model; and carrying out risk early warning on the target event based on the value-at-risk information of each target sub-object in each time period. According to the method, the adaptive iteration risk assessment model is constructed based on dynamic data driving, so that leap-type upgrading from static offline assessment to real-time dynamic early warning is realized, and the timeliness and accuracy of risk identification are remarkably improved.

Various methods and processes, apparatuses / systems, and media for generating recourse data for a denied applicant are disclosed. A processor trains a machine learning model by using a first set of training data and a second set of training data which outputs risk classification data associated with a negative decision; identifies, based on the risk classification data, the denied applicant who received the negative decision; identifies individuals whose applications were initially rejected but approved later based on accessing historical data from a database; groups the individuals, whose applications were initially rejected but approved later, into a high density cluster by applying a clustering algorithm; and generates, by applying a computing algorithm, a recourse data for the denied applicant utilizing similar individuals within the high density cluster whose applications were initially rejected but approved later and whose features values at initial rejection are similar to features values of the denied applicant.

The invention discloses a photovoltaic cluster power prediction method based on step-by-step spatial feature clustering and an improved graph attention network, and relates to the photovoltaic field, and the method comprises the following steps: constructing a distributed photovoltaic cluster data set, the method comprises the following steps: performing primary clustering division by taking the physical characteristics of a photovoltaic module as characteristics to be input into an affinity propagation AP algorithm, then performing secondary clustering division by taking a solar altitude angle sequence as characteristics to be input into the AP algorithm, and finally dividing a photovoltaic cluster into a plurality of sub-clusters; for each photovoltaic sub-cluster, sorting and merging historical power and historical meteorological time sequence data, and inputting the historical power and historical meteorological time sequence data into a GAT-Encoder-Decoder deep learning model for training; reasoning and outputting a day-ahead power prediction result of each power station in the sub-cluster; and accumulating the prediction results of all the power stations to obtain a power prediction result of the whole photovoltaic cluster. According to the invention, clustering calculation is carried out through step-by-step spatial features so as to obtain a sub-cluster division result which can better reflect the spatial feature state of the photovoltaic power station, and the correlation of the output of the photovoltaic power station in the sub-cluster is improved.

The invention provides a carbon future price prediction method and system based on multi-level feature engineering and bidirectional gating. According to the invention, accurate prediction of the carbon future price is realized through data preprocessing, multi-level feature engineering, BiGRU construction, point prediction and interval prediction. Wherein the multi-level feature engineering integrates original features, statistical features, differential features and similarity features, the similarity features are extracted through a probability density network (PDN) and random walk similarity propagation (SRW), and the long-range dependency capture capability is enhanced. According to the method, the long-range dependency recognition capability of the model is remarkably enhanced through the probability density network and the random walk similarity propagation method, and the probability density network constructs a global similarity matrix by quantifying the distribution similarity between time windows; the node indirect relation is captured through random walk similarity propagation, the similarity features enable the model to utilize historical similar situation information, and ablation experiments prove that the MAE of the model containing the similarity features is reduced by about 6.1%.