446 results about "Extreme learning machine" patented technology

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention discloses a transformer fault diagnosis method based on multi-sensor multi-domain feature fusion, and the method comprises the following steps: 1, obtaining a vibration signal data set of a transformer box, and carrying out the decentralization of a collected signal; 2, decomposing the decentralized transformer vibration signal by adopting a dung beetle optimized ICEEMD method, calculating a Pearson correlation coefficient between an IMF component and an original signal, and selecting a strongly correlated component to reconstruct the transformer vibration signal, so as to filter and denoise the transformer vibration signal; step 3, extracting time domain, frequency domain and space domain features in the transformer vibration signals by adopting an MVAR model, VMD-BIS decomposition and Riemannian manifold, and constructing a joint feature vector; 4, performing fusion dimension reduction by adopting a kernel principal component analysis algorithm to obtain a fusion dimension reduction feature vector; and a fifth step of constructing a kernel extreme learning machine model, training parameters of the kernel extreme learning machine model in combination with the time-frequency-space fusion features, and optimizing hyper-parameters of the kernel extreme learning machine model in combination with a Harris eagle optimization algorithm so as to obtain an optimal kernel extreme learning machine classification model. According to the method, the problems of misjudgment, missed judgment and low diagnosis rate caused by unbalanced distribution due to nonlinear characteristics of vibration signals in transformer fault diagnosis are effectively solved.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention discloses a lithium battery diagnosis method and system based on multivariable and infrared point cloud. The method comprises the steps that voltage, current, temperature, voiceprint and infrared three-dimensional point cloud data of a lithium battery are collected through multiple sensors; performing mode decomposition on the electric signal by adopting symplectic geometric mode decomposition SGMD, and constructing a two-dimensional input matrix in combination with an original signal; an electric signal is processed by using a Tokenformer model optimized by CBAM, and image data is analyzed by using a YOLOv9 model improved by ECA; integrating the electric signals and the image features through a PTP multi-mode fusion method; and finally, an improved extreme learning machine IELM is adopted to realize fault diagnosis. According to the method, through a multi-modal coupling structure and intelligent algorithm optimization, the diagnosis precision and the calculation efficiency are remarkably improved, meanwhile, the dependence on labeled data is reduced, and the generalization ability and the adaptability of the model are enhanced.

The invention relates to the field of electric digital data processing, in particular to a building digital twin data processing method based on artificial intelligence, which comprises the following steps of: acquiring real building digital twin data, training a data expansion model by adopting a generative adversarial network algorithm based on topology dual optimization to obtain training data, and calculating the real building digital twin data according to the training data. Obtaining a central building energy consumption evaluation model and a local building energy consumption evaluation model by adopting a distributed federated learning method, training the local building energy consumption evaluation model of each building by adopting a self-adaptive constraint-based extreme learning machine algorithm, continuously iterating the local building energy consumption evaluation model, and evaluating the energy consumption of each building. And evaluating new real building digital twin data by using the trained local building energy consumption evaluation model. An existing building digital twin data processing method has the problem that energy consumption grade evaluation is not accurate, and the method is accurate in energy consumption grade evaluation.

The invention discloses a strain culture monitoring method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the following steps: collecting multi-dimensional original data in a fermentation process, and carrying out the preprocessing to construct a multi-dimensional feature data set; training a bidirectional LSTM model through the multi-dimensional feature data to obtain a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector through a sparse self-encoding technology to obtain a low-dimensional feature vector, and processing the low-dimensional feature vector through phase mapping and an extreme learning machine to obtain a target classification result; and constructing a Markov decision model based on the low-dimensional feature vector and a target classification result, and solving the Markov decision model through a reinforcement learning algorithm to obtain an optimal adjustment strategy. According to the scheme, intelligent monitoring and precise regulation and control of strain culture based on artificial intelligence are achieved, the defects of a traditional method in data utilization, stage adaptability and model generalization ability are overcome, and the intelligent level and production efficiency of strain culture are improved.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

In order to solve the problems of high energy consumption and unbalanced comfort of a traditional static control strategy of the air conditioning system during environment change, a building three-dimensional model is constructed, and sensor data and energy consumption simulation software are fused to construct a joint simulation environment. The improved extreme learning machine neural network is used for predicting the air conditioner energy consumption and the personnel thermal comfort degree, the network structure and parameters are optimized through the rotary particle swarm optimization, and the prediction precision and generalization ability are improved. And then, in combination with a Newton-Raphson multi-objective optimization algorithm, solving an air conditioner temperature set value optimal dynamic sequence in a rolling manner, and balancing energy consumption and comfort through a Pareto frontier solution set. Through dynamic simulation and data driving optimization, precise regulation and control of the air conditioner are achieved, and the thermal comfort degree is improved.

The invention discloses a computer network security data processing method and system based on artificial intelligence, and the method comprises the steps: building a multi-channel deep learning fusion model, extracting spatial local features in a traffic sequence through employing a 1D-CNN one-dimensional convolutional neural network, capturing a long-range time sequence dependence relation between log events, and carrying out the recognition of the long-range time sequence dependence relation between log events; modeling the user operation behavior sequence based on an LSTM (Long Short-Term Memory) network, and fusing the feature weight by using an attention mechanism to obtain a fused feature vector; inputting the fusion feature vector into a classifier established based on an OS-ELM online sequence extreme learning machine to perform real-time threat assessment, and outputting a probability index of network attacks occurring in a short time in the future; and generating a cooperative defense decision according to the network attack probability index, and sending the cooperative defense decision to security equipment for execution. Excessive defense or insufficient protection is avoided, and the cooperation efficiency of safety equipment is remarkably improved.

The invention provides a pipeline defect assessment method and system based on multi-source data fusion, and the method comprises the following steps: collecting the data of the operation state of a pipeline body through a related collection device, and marking the state and fault type of the pipeline, so as to construct a training set and a test set; and preprocessing the static data of the pipeline, wherein the preprocessing comprises data cleaning, standardization and feature extraction. And optimizing the extreme learning machine by using a bird flock algorithm, analyzing static data and evaluating pipeline defects. Preprocessing the image data of the pipeline body, wherein the preprocessing comprises image conversion, enhancement and denoising; and evaluating the circumferential weld defect based on the pre-trained VGG16 model. And performing decision-level fusion on a defect evaluation conclusion obtained by static data analysis and circumferential weld image analysis by using a D-S evidence reasoning algorithm to obtain a final evaluation result. The method effectively improves the accuracy and reliability of pipeline defect evaluation, and has wide application prospects and important practical value.

The invention discloses a self-adaptive ultrasonic measurement method and system based on multi-channel cooperation, and relates to the technical field of ultrasonic measurement. The method comprises the following steps: collecting original flight time signals of each channel; meanwhile, environment data are collected in real time; original flight time signals are processed, effective signal arrival pre-flight time is extracted, and a fusion data set is constructed; constructing a two-dimensional sound channel spectrogram based on the multi-sound channel time sequence of the pre-flight time, and performing feature extraction based on a lightweight convolutional neural network to generate an abnormal confidence vector; analyzing the sound channel state based on the abnormal confidence vector; constructing a physical information neural network, and analyzing the corrected sound velocity value of each sound channel and the two-dimensional sound velocity field distribution on the section of the whole pipeline; training an online sequence extreme learning machine model in combination with historical measurement data; and based on the final fusion weight of each sound channel and the corresponding sound channel flow velocity, carrying out weighted fusion to generate a flow velocity optimal estimation value. And the measurement precision and robustness are improved.

The invention relates to the field of power optical cable facilities, in particular to a power optical cable fault detecting and positioning method based on an extreme learning machine. The OTDR signal is subjected to multi-scale decomposition and de-noising processing based on wavelet packet analysis, and high-frequency noise is effectively suppressed and key event point features are reserved by refining frequency band division, so that the signal quality is improved. And constructing a fault classification model based on an extreme learning machine, and realizing efficient classification and fault mode identification of de-noised OTDR data. And in combination with a white whale optimization algorithm (BWO), carrying out optimization search on a fault detection result output by the classification model so as to improve the detection precision and algorithm robustness in a complex optical cable environment. And an improved Dijkstra algorithm is introduced to realize accurate positioning and path tracking, so that the accuracy and response speed of fault positioning are remarkably improved. Through multi-level algorithm fusion, the problems of signal noise interference, insufficient pattern recognition accuracy and low positioning precision in a complex network environment in optical cable fault detection are solved.

The invention provides an intelligent machine tool cutter wear state recognition and machining parameter adjustment method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting the working condition data of a machine tool machining process, carrying out the sliding window segmentation, denoising and normalization processing, extracting a time sequence feature sequence through a bidirectional gating circulation unit, and carrying out the recognition of a machining parameter; the method comprises the following steps: acquiring an intrinsic mode component by combining Hilbert-Huang transform decomposition, extracting a frequency domain feature sequence by utilizing four-layer spatial pyramid pooling, determining a fusion weight based on mutual information entropy to obtain a tool wear state feature vector, and inputting the feature vector into a pre-training extreme learning machine to predict the wear loss and the residual life. And determining an initial adjustment scheme in combination with observation sharing information of a parallel agent structure, predicting a processing quality score through a capsule network and feeding back the score as a reward signal, and obtaining an optimization correction strategy for adjusting working parameters of a machine tool.

The invention discloses a multi-scale width ELM lithium battery RUL prediction model and method based on an elastic network. The prediction model comprises a CEEMDAN multi-mode decomposition module, a multi-extreme learning machine module and an elastic network regularization width neural network module; a CEEMDAN multi-mode decomposition module decomposes battery recession data into a plurality of intrinsic mode IMFs, extracts multi-scale feature information through a multi-scale feature extraction module, inputs the extracted multi-scale feature information into an ELM module for training, selects a plurality of better ELMs based on a plurality of performance indexes, and finally, takes prediction results of the plurality of ELMs as new features, so that the performance of the battery is improved. And inputting the two features into an elastic network regularization width neural network module through enhancement features of width expansion to obtain a final prediction result. According to the method, the training data volume can be effectively reduced, the calculation complexity can be remarkably reduced while the prediction precision is improved, and the real-time application of the system is ensured.

The invention discloses an Alzheimer disease risk prediction method based on feature fusion, a medium and equipment. The method comprises the following steps: firstly, acquiring an sMRI image, an FDG-PET image, a biomarker and intelligence score data of a target subject; then, sMRI and FDG-PET image data are respectively input into a three-dimensional convolutional neural network module, and a corresponding first image feature map and a corresponding second image feature map are extracted; secondly, inputting the two feature maps into a cross attention fusion module to obtain image fusion features; and inputting a U-shaped up-sampling network to segment a hippocampus region to obtain hippocampus features. And then, merging the image fusion features, the hippocampus features, the biomarker data and the intelligence score data, and inputting the merged data into an extreme learning machine for calculation, thereby obtaining an onset risk prediction result of the Alzheimer's disease. According to the method, by means of cross-modal data fusion, multi-modal data complementary information is extracted, and the accuracy of onset risk prediction can be effectively improved.

The invention discloses a distributed photovoltaic power prediction method and system, belongs to the technical field of renewable energy prediction, and solves the prediction precision and efficiency bottlenecks of a traditional model under complex meteorological conditions through fusion of deep feature learning and an adaptive optimization mechanism. Firstly, a time sequence sample set is constructed based on a sliding window mechanism, and non-stationary fluctuation characteristics of a power sequence are dynamically captured; eliminating the dimensional difference between the input features and the tags through minimum-maximum normalization; constructing a four-level restricted Boltzmann machine stacking structure, and extracting time-space coupling characteristics of power data by layer-by-layer unsupervised pre-training; and designing an improved extreme learning machine dynamic analysis architecture, optimizing the number of neurons in a hidden layer in combination with grid search, and realizing global optimal balance between model complexity and prediction precision. According to the method, an effective dynamic mode and noise interference are distinguished by using hierarchical feature abstraction capability of the RBM, prediction robustness in strong fluctuation scenes such as cloudy and rainy scenes is remarkably improved through analytical solution and parameter adaptive adjustment of the ELM, and efficient technical support is provided for intelligent scheduling and energy storage optimization in a high-proportion photovoltaic grid-connected background.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention belongs to the technical field of agricultural food detection, and particularly relates to a method for rapidly detecting the fatty acid content of Beijing ducks based on a near infrared spectrum technology, which comprises the following steps: selecting a sample and pre-treating the sample; collecting sample data by using a standardized collection method; removing abnormal samples; performing spectral data correction; standard normal variable transformation is used to eliminate baseline drift and random errors in the spectral data; dividing the data set into a training set and a test set according to a proportion of 7: 3 by using a sample set division method based on a joint X-Y distance; constructing an optimized extreme learning machine model, and training the optimized extreme learning machine model based on the training set; constructing an evaluation model, and verifying the detection effect of the optimized extreme learning machine model based on the test set; rapid detection of the fatty acid content of the Beijing duck is realized. The defects that an existing method is long in consumed time and lagged in detection result, and dangerous solvents need to be used are effectively overcome.

The invention provides a hot rolled strip convexity prediction method based on weight distribution strategy hybrid modeling, and relates to the technical field of metallurgical machinery and automation. The method comprises the following steps: acquiring historical production data in the rolling process of a hot-rolled strip, and establishing a convexity mechanism model of the hot-rolled strip; an improved ant colony algorithm is adopted to optimize the extreme learning machine, and a hot rolled strip convexity data driving model is constructed; calculating the information entropy of the mechanical model and the information entropy of the data driving model by adopting an entropy weight method; according to the model information entropy, calculating an initial weight coefficient of a mechanical model and an initial weight coefficient of a data-driven model, and establishing a hybrid model through a weighted summation method; and in hot-rolled strip production of different rolling units, the weight is dynamically adjusted according to the deviation value between the predicted value and the actual value of the mechanism model and the data driving model, and a final hot-rolled strip convexity prediction result is output. By adopting the method, the accuracy of convexity prediction of the hot-rolled strip can be improved.

The invention discloses a power distribution network fault positioning method and positioning system, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: collecting multiple groups of historical data of different faults of a power distribution network, obtaining the missing intervals of voltage and current sequences in each group of historical data, and positioning the missing intervals to the front, middle and back of the whole sequence; different interpolation methods of different missing intervals are obtained by combining the length of the missing interval, the distance from the starting point position of the missing interval to the previous nearest missing interval and the distance from the ending point position of the missing interval to the next nearest missing interval, an abnormal value is marked for a sequence after interpolation is completed, and the sequence is equally divided into S < front > and S < back >; the abnormal values are repaired by utilizing ELM, a standard data set belonging to the fault is obtained, clustering processing is carried out on data in the standard data set, irrelevant clusters are removed, and a specific position is positioned according to the similarity between the fault and each group of faults in the target cluster. The power distribution network fault positioning method effectively improves the power distribution network fault positioning precision.

The invention relates to the field of monitoring in electric digital data processing, in particular to a health monitoring equipment data classification and identification method based on machine learning, which comprises the following steps: acquiring multiple groups of equipment data through multiple different types of sensors, and matching a corresponding state tag for each group of equipment data; performing dynamic segmentation normalization on the equipment data by adopting a sliding normalization strategy based on a dynamic time window to obtain normalized equipment data; inputting the normalized equipment data into different classification models constructed based on a multilayer extreme learning machine principle, and training the classification models to obtain trained classification models; and performing dynamic segmentation normalization processing on newly-collected equipment data, and inputting the newly-collected equipment data into the trained classification model to obtain a state tag corresponding to the newly-collected equipment data. An existing health monitoring equipment data classification and recognition method has the problem that classification and recognition are not accurate enough, and the method is accurate in classification and recognition.

The invention provides an intelligent safety helmet operation fatigue risk early warning classification algorithm based on a fractional order depth extreme learning machine. Belongs to the technical field of intelligent wearable equipment and artificial intelligence. The classification algorithm is executed through the following method, and the method comprises the steps that original data are obtained in real time through multiple sensors integrated on the intelligent safety helmet, and the original data comprise personnel physiological data and environment data; the method comprises the following steps: preprocessing collected original data, performing fractional differential or integral processing on the data by using a fractional calculus theory, and extracting deeper information features; by means of multiple sensors (such as heart rate, brain wave, acceleration and environment sensors) integrated on the intelligent safety helmet, physiological data and environment data of an operator can be obtained in real time.

The invention discloses a mechanical system structure health monitoring method and device based on hierarchical symbol transfer entropy, and relates to the technical field of data processing, and the method comprises the steps: collecting the vibration data of a plurality of channels of an electromechanical system in different health states; calculating a hierarchical symbol transfer entropy of each sub-channel vibration signal in the plurality of channel vibration data; performing superposition calculation on the level symbol transfer entropy of each level of each sub-channel vibration signal to obtain a second-order tensor feature corresponding to each sub-channel vibration signal; superposing the second-order tensor features corresponding to the vibration signals of each sub-channel to obtain second-order tensor feature matrixes corresponding to the vibration data of the multiple channels; training the initial two-dimensional extreme learning machine based on the second-order tensor feature matrix belonging to the training data set to obtain a trained two-dimensional extreme learning machine; the health state of the electromechanical system is monitored based on the two-dimensional extreme learning machine, and the accuracy of monitoring the health state of the electromechanical system is improved.

The invention discloses a lithium battery health monitoring and early warning method based on multi-sensor data fusion, and the method comprises the steps: arranging a multi-measurement-point and multi-variable sensor in a lithium battery pack, collecting the data of a lithium battery in real time, carrying out the normalization processing, carrying out the feature selection through a random forest algorithm, and constructing a lithium battery thermal runaway health monitoring data set; a lithium battery thermal runaway health monitoring and early warning model MCCN-TimeMixer based on a multi-scale causal convolutional network MCCN and a TimeMixer model is constructed, local thermal state change features are extracted through the MCCN, and then a global relation between the features and a lithium battery thermal runaway health monitoring state is established through the TimeMixer; a hedera helix algorithm improved by a multivariate learning algorithm is adopted to optimize model hyper-parameters, finally error correction is performed through a fast deep stacked network extreme learning machine FDSNELM, and accurate prediction and early warning of the lithium battery thermal runaway health monitoring state are realized. The method can be applied to the modeling process of lithium battery thermal runaway health monitoring and early warning, and the accuracy of lithium battery thermal runaway health monitoring and early warning is ensured.

The invention relates to the technical field of internet big data, in particular to an electric power high-altitude worker working state detection method based on image feature fusion, which comprises the following steps: S1, acquiring a to-be-detected original image; s2, inputting the original image into an OpenPose network model to perform human body posture feature extraction to obtain human body posture key point features of each person target in the original image; s3, inputting the original image into a YOLACT model to perform power equipment feature extraction to obtain power equipment features; s4, performing feature fusion on the human body posture key point features and the power equipment features of each personnel target to obtain fusion features of each personnel target; and S5, inputting the fusion features of each personnel target into a trained online sequential extreme learning machine for classification, judging whether each personnel target is performing high-altitude electric power operation or not, and outputting the personnel target performing high-altitude electric power operation in the original image. The detection precision of the working state of the electric power high-altitude worker can be improved.

The invention discloses a DC motor fault diagnosis method based on improved local mean decomposition and composite multi-scale bubble entropy fusion, and relates to the technical field of motor fault diagnosis. In order to solve the technical defect of insufficient fault feature extraction of the existing motor fault diagnosis technology in the prior art, the technical scheme provided by the invention comprises the following steps: collecting a sound signal in a running state of a direct current motor as an original signal; decomposing the original signal by adopting an improved local mean decomposition method to obtain a plurality of product components, and screening out effective feature components with high correlation with the original signal; performing composite multi-scale bubble entropy calculation on the effective feature component, and extracting a multi-dimensional entropy feature vector representing signal complexity; and inputting the multi-dimensional entropy feature vector into a particle swarm optimization extreme learning machine model for classification so as to obtain a fault diagnosis result of the DC motor. The method can be widely applied to real-time monitoring and intelligent fault identification of the running state of the direct-current motor in industrial production.

The invention discloses a data security management method and system based on a cloud computing platform, and relates to the technical field of data security management, and the method comprises the steps: collecting original data of the cloud computing platform in real time, and carrying out the preprocessing; constructing a discrimination model based on a generative adversarial network (GAN) to generate a forged data sample, taking the forged data sample and a real data sample as input data, optimizing the discrimination model by using the input data based on an extreme learning machine, and discriminating attack data; and constructing a security situation model by using a hidden Markov model, optimizing parameters through a cluster particle algorithm, and outputting a predicted security situation based on real-time attack data. A discrimination model is optimized through a generative adversarial network and an extreme learning machine, the discrimination precision of attack data is effectively improved, a security situation model is established through a hidden Markov model, model parameters are optimized in combination with a cluster particle algorithm, a predicted attack path is combined with a deep Q learning network, and the security risk of attack data is improved. And attack path prediction is carried out through the dual deep Q network, and a defense strategy is adjusted in real time.

The invention discloses a transformer fault diagnosis method based on an improved Harris eagle optimization multi-core extreme learning machine, and the method comprises the following steps: 1, carrying out the data enhancement of a minority class of samples in transformer fault samples through employing an SMOTE algorithm, and enabling each class of samples to achieve the class balance; 2, normalizing the balanced sample data, and dividing the balanced sample data into a training set, a verification set and a test set; 3, selecting a plurality of different kernel functions to construct a multi-kernel extreme learning machine classification model, and training the multi-kernel extreme learning machine classification model by using the training set; step 4, optimizing hyper-parameters of the multi-core extreme learning machine by adopting an improved Harris eagle algorithm, and obtaining an optimal multi-core extreme learning machine model by taking an identification error of the verification set as a fitness function; and 5, testing the classification performance of the optimal multi-core extreme learning machine model by using the test set. According to the method, the nonlinear representation of the transformer fault sample is effectively enhanced by using the multi-core extreme learning machine, and the accuracy and reliability of transformer fault diagnosis can be improved; meanwhile, the robustness of the multi-core extreme learning machine is effectively enhanced by using the improved Harris eagle optimization algorithm with higher convergence speed and higher search capability of parameter optimization.

The invention provides an online sequential preposed interference layer extreme learning machine, a classification method, equipment and a medium, and relates to the technical field of machine learning. The online sequential preposed interference layer extreme learning machine comprises an input layer, an interference layer, a hidden layer and an output layer, the input layer is used for receiving original sample data, the interference layer reduces the sample distribution complexity through nonlinear kernel mapping, the hidden layer is located between the interference layer and the output layer, and the output layer is located between the interference layer and the output layer. And the output layer is used for further extracting features of the samples mapped by the interference layer and calculating a final classification result based on the output of the hidden layer. According to the method, the performance of the extreme learning machine in classification and regression tasks can be remarkably improved, particularly, higher generalization ability and stability are shown when nonlinear distribution samples are processed, meanwhile, dependence on parameter selection is reduced, and the method is suitable for real-time data processing scenes such as intelligent medical treatment, fault detection and financial prediction.