86 results about "Kernel extreme learning machine" patented technology

The invention discloses a kernel extreme learning machine flood forecast method based on sparse self-encoding. The method comprises the following steps that (1) medium and small river flood data are selected, and the data are collated and cleaned; (2) the appropriate forecast factors are selected and samples are collated, and the sample data are preprocessed; (3) unsupervised learning is performedon the original sample data through a multilayer sparse self-encoder, and the optimal network layer parameters are trained; and (4) the sample data through multilayer sparse self-encoding learning act as the input of a KELM model, an SAE_KELM model is constructed and the corresponding result is predicted and evaluated. The SAE method is fused on the basis of the KELM model, a deep network model is constructed, and the abundant intrinsic information between the complex data can be learnt by increasing the number of layers of the model. The "essential" characteristics of the original hydrological data can be learnt by the SAE_KELM model, the learnt characteristics have more essential depiction for the data, and the mapping relationship between the characteristic value and the target value can be better fit by the KELM model.

The invention discloses a bearing fault diagnosis method combining an improved sparse filter and a KELM. The method comprises the following steps: embedding a Min-Max regular term into an original sparse filter to obtain an improved sparse filter. The Min-Max regular term can describe the internal structure information of the original data, and promotes the similar samples to be close to each other and promotes the samples of different classes to be separated from each other, thereby generating discriminative characteristics. Feature discrimination mainly lies in that class label information is used in construction of the Min-Max regular term, and a pseudo label is used for replacing a real label to guide the construction of the Min-Max regular term. Vibration signals of different operation conditions of a rolling bearing are collected to serve as a training set, the training set is used for training an improved sparse filter model and a kernel extreme learning machine model to obtainmodel parameters, and therefore establishment of a fault diagnosis classification model is completed, and the diagnosis model can accurately identify the rolling bearing fault.

The invention discloses a gas path fault diagnosis method and system for aeroengine dynamic process. The method includes the steps of establishing a feature extraction model based on a multi-layer kernel extreme learning machine; adopting a hidden Markov model based on time series modeling for fault identification. The method solves the problem that the conventional data-based engine fault diagnosis uses time series measurement data to diagnose the fault with low accuracy in the existing aeroengine gas path fault diagnosis in the dynamic process, is suitable for the engine dynamic fault diagnosis in consideration of the degradation of gas path components and the redundancy of sensor parameters, and has a positive promotion effect on engine health management and maintenance cost reduction.

The invention discloses a random forest classification method used for coronary heart disease data classification and based on a kernel extreme learning machine and parallelization. Sampling with replacement is performed on a coronary heart disease sample set by using a Bootstrap method so that different coronary heart disease data training subsets and test subsets are generated to be used for base classifiers; the kernel function of the hybrid kernel form is used as the kernel function of the kernel extreme learning machine so that the influence of the kernel type on the performance of the classification model can be reduced; model training is performed on the kernel extreme learning machine by using the coronary heart disease data training subsets and performing testing is performed on the base classifiers by using the test subsets, cyclic judgment is performed by using the mode of sorting and particle swarm optimization and the optimized new base classifiers are regenerated and thebase classifiers having poor classification performance are eliminated and substituted so that the objective of enhancing the overall classification performance can be achieved; and the random forestmodel is formed and then the classification result is selected by using a relative majority voting method.

The invention discloses a satellite power source main bus-bar current interval prediction method. According to the method, based on a prediction model trained by an optimized kernel extreme learning machine, a prediction interval is determined by using a proportionality coefficient method, and the parameters of the proportionality coefficient method are optimized by using a differential evolution algorithm. The method specifically includes the following steps that: satellite power source main bus-bar bus current data are preprocessed, noise data can be removed, and normalized data can be obtained; the parameters of the kernel extreme learning machine are optimized by adopting the differential evolution algorithm; the optimized kernel extreme learning machine is adopt to construct an initial prediction model; comprehensive indexes for evaluating the quality of the prediction interval are given, the prediction interval is determined through adopting the proportionality coefficient method, and the satisfaction degree of the prediction interval is evaluated; and the prediction proportionality coefficient of the interval is optimized through using the differential evolution algorithm, so that an optimal satellite power source main bus-bar current prediction interval can be obtained. The satellite power source main bus-bar current interval prediction method of the invention is based on complicate satellite power source main bus-bar current data, and has the advantages of higher prediction accuracy and better effect.

The invention discloses a fault diagnosis method for a centrifugal pump rotor system, particularly relates to a fault diagnosis method for the centrifugal pump rotor system based on variational mode decomposition and a kernel extreme learning machine, and belongs to the field of rotary machine fault diagnosis. The method mainly comprises the steps that S1, acquiring vibration acceleration signals(normal, rotor misalignment, rotor imbalance, bearing inner ring faults, bearing outer ring faults and bearing rolling body faults) of a centrifugal pump rotor system in a normal state and a fault state, and obtaining a time domain signal sample set; S2, carrying out variational mode decomposition on the obtained time domain signal sample set to obtain an intrinsic mode function component; S3, solving an energy value, a waveform factor, a pulse index, a margin coefficient, a peak factor and a kurtosis value for each intrinsic mode function component; S4, constructing a feature matrix, and normalizing the processed data; S5, training the fault diagnosis system of the centrifugal pump rotor system by adopting the training sample; S6, inputting the test sample or the real-time sample into thecentrifugal pump rotor system fault diagnosis model to diagnose the fault.

The invention relates to wireless locating. For improving the locating precision, reducing the sample data dimension and increasing the locating speed, a locating prediction model is obtained. The technical scheme adopted by the invention is an improved kernel extreme learning machine locating method. The method comprises the steps of firstly, obtaining training data by adopting a method for performing multi-time measurement in a same position; secondly, dividing the data measured in the same position into a sample sub-space, extracting features of the sample sub-space, and replacing originaltraining data with the features of the sample sub-space; meanwhile, improving a kernel extreme learning machine algorithm by utilizing related theories of matrix approximation and matrix extension; and finally, training the obtained processed training data by utilizing an improved kernel extreme learning machine to obtain the locating prediction model, and performing position estimation by using the obtained locating prediction model to achieve the purpose of locating. The method is mainly applied to the wireless locating occasion.

The invention is suitable for a field of information technology and provides a model parameter optimizing method and device. The method includes obtaining sample data and performing standardizing treatment on the sample data; obtaining the optimal penalty coefficient C and the optimal kernel scale Gamma of a kernel extreme learning machine through combining with a Moth optimization algorithm of chaos theory by adopting the sample data subjected to standardization; according to the sample data subjected to standardization, the optimal penalty coefficient C and the optimal kernel scale Gamma, constructing a target classification predication model. The invention solves a problem that the optimal penalty coefficient C and the optimal kernel scale Gamma cannot be obtained by utilizing a grid searching method in the prior art and is beneficial to improvement of effect of the constructed model in classification and predication of determined problems.

The invention discloses a kernel extreme learning machine based quick traffic sign detecting method and belongs to a field of image signal processing and mode recognition. The method includes reading an original sample image; utilizing a BING based objectness method for producing an area that may contain traffic signs; extracting HOG features of the candidate area and sending the features to a kernel extreme learning machine classifier; and obtaining a final detection result. According to the invention, a traditional slide window scanning method is abandoned. The BING algorithm is used for reducing search space and improving detection speed. A traditional ELM algorithm has a single hidden layer structure and has huge boundedness in complicated signal analysis. The invention adopts KELM (Kernel Extreme Learning Machine) for classification detection. The kernel extreme learning machine improves the stability of a learning model and enhances the generalization performance, improves the detection performance and keeps an advantage of low time consumption of ELM (Extreme Learning Machine).

The invention discloses an emotion recognition method and system based on electroencephalogram signals. The method comprises the following steps: acquiring to-be-identified multi-channel electroencephalogram signals that are electroencephalogram signals from multiple channels when a to-be-identified person watches videos capable of stimulating different emotions; carrying out feature extraction onthe to-be-identified multi-channel electroencephalogram signals based on a discrete wavelet transform algorithm to obtain electroencephalogram features, including frequency band entropies and frequency band energy, of all channels; according to a minimum redundancy maximum correlation algorithm, performing feature selection on the electroencephalogram features to obtain electroencephalogram feature selection signals; and classifying the electroencephalogram feature selection signals by adopting a kernel extreme learning machine algorithm to obtain an electroencephalogram signal emotion recognition result. The emotion recognition precision can be improved.

The invention provides an online predicting method for silicon content of blast furnace molten iron. According to a blast furnace silicon element transferring mechanism, a parameter which affects of silicon content of the molten iron is selected from blast furnace operation parameters as an input variable of a predicting model. A Pearson correlation analysis method is used for determining a lag time length between the input variable and the silicon content of the molten iron. Then standardizing processing is performed on the sample data and the predicting data of the input variable. The influence of different dimensions to model predicting accuracy is eliminated. A kernel extreme learning machine is utilized for predicting the silicon content of the molten iron at a next time point. A sliding window updating method is used for performing online updating on training set data. A genetic algorithm is introduced for optimizing the key parameter of the kernel extreme learning machine model.The online predicting method according to the invention is suitable for long-time online predicting to the silicon content of the blast furnace molten iron. An actual testing result proves a fact that the predicting method according to the invention has relatively high predicting precision. The method facilitates advanced understanding of the silicon content of the blast furnace molten iron by blast furnace operators, thereby adjusting the operation parameters in time.

The invention discloses a gait recognition method based on an extreme learning machine. The method comprises the steps of preprocessing, feature extraction and classification recognition, and specifically comprises the following steps that the preprocessing is used for obtaining a standard moving target contour sequence with the uniform size, and the steps are included and comprises: 1-1, extracting a moving target contour sequence; 1-2, image standardization; The feature extraction is used for obtaining gait feature parameters with good characterization, and comprises the following steps: 2-1, gait period extraction; 2-2, extracting an action energy diagram; 2-3, dimension reduction is carried out through two-dimensional principal component analysis; Classification recognition is carriedout by adopting a kernel extreme learning machine (KELM); 3-1, constructing a kernel extreme learning machine neural network model; 3-2, training a neural network of the kernel extreme learning machine; 3-3, performing classifying and identifying. The action energy diagram extracted by the method contains more dynamic and static information, a complex image processing process is not needed, the extraction mode is simple, and the method has very good characterization.

The invention relates to the technical field of hyperspectral image classification, and discloses a hyperspectral image classification method based on guided filtering and a kernel extreme learning machine, and the method comprises the following steps: S1, carrying out the normalization preprocessing of hyperspectral image data; s2, performing principal component analysis on the normalized hyperspectral image data to obtain a plurality of different principal component images; s3, performing guided filtering processing on the input image through the guided image, and extracting space-spectral information; s4, selecting 5% of the images after guided filtering processing as a training set, and taking the remaining images as a test set; s5, performing linear weighting on the space spectrum kernel and the spectrum kernel in a linear weighting mode to form a weighting kernel; s6, the weighted kernel is sent to a kernel extreme learning machine, then the trained kernel extreme learning machine is used for carrying out classification prediction on the test sample, and the hyperspectral image classification method has the advantages that the classification precision is very high, and the time cost spent in the whole classification process is very low.

The invention relates to an organic pollutant migration numerical model substitution method based on a multi-core extreme learning machine, and the method comprises the following steps: building an underground water organic pollution multiphase flow migration numerical model according to observation data, determining pollution source characteristics, aquifer parameters and the value range of each variable, which have relatively high contribution degree to the spatial and temporal distribution of pollutants, in the model; preparing a training sample set; training a single-kernel extreme learning machine substitution model of the multiphase flow numerical model; establishing a nonlinear programming optimization model of kernel function key parameters and kernel function combination weights; and solving the optimization model by using a genetic algorithm, identifying an optimal kernel function parameter and a combination weight, and constructing a genetic evolution multi-kernel extreme learning machine intelligent substitution model of the numerical model. The problem of substitution of the underground water organic pollution multiphase flow numerical model is solved, the calculation efficiency of pollutant transport simulation prediction is improved, and an efficient solution is provided for underground water pollution source characteristic and pollutant transport parameter inversion identification.

The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to a reciprocating compressor fault diagnosis method based on improved RCMDE, which comprises the following steps that machine body surface time domain vibration signals of a reciprocating compressor under different operation conditions are acquired, and an initial vibration signal is processed by adopting parameter optimization variational mode decomposition to obtain an intrinsic mode function component; according to a kurtosis-correlation coefficient criterion, a group of components containing the most abundant information amount is selected, and a fault signal is reconstructed; the nonlinear behavior of the vibration signal after noise reduction is quantitatively analyzed through the improved fine composite multi-scale dispersion entropy, and a fault feature vector is formed; input features are selected in a dimensionality reduction mode through a kernel principal component analysis method and input into a kernel extreme learning machine to be classified and recognized, and the operation state and the fault type of the reciprocating compressor can be distinguished. Through improved fine composite multi-scale dispersion entropy analysis, nonlinear behaviors of vibration signals are quantitatively described, feature vectors are formed, and fault types can be diagnosed more accurately.

The invention discloses a short-term wind power generation prediction method and device, equipment and a storage medium, and relates to the field of new energy power generation prediction, and the method comprises the following steps: obtaining main meteorological characteristics affecting the generated power, and combining the historical data of the main meteorological characteristics with the historical data of the generated power to obtain a target data set; setting and initializing parameters of the multivariate universe algorithm and the kernel extreme learning machine; dividing the target data set into a training set and a test set, and training a kernel extreme learning machine by using the training set; running a multivariate universe algorithm to obtain an optimal combination of penalty parameters and kernel parameters in the kernel extreme learning machine; and substituting the optimal combination into a kernel extreme learning machine, and testing the test set by using the kernel extreme learning machine with the optimal combination to obtain a prediction result. According to the scheme, the parameters of the kernel extreme learning machine are optimized by using the multivariate universe algorithm, the problems of local optimal solution and the like can be avoided, and the prediction precision of the model is improved.

The invention belongs to the technical field of mechanical operation state trend prediction, and particularly relates to a method for predicting the stability of an angular contact ball bearing retainer. The method comprises the following steps: S100, calculating the instability of the retainer; S200, constructing a kernel extreme learning machine KELM model; S300, training the KELM model; And S400, predicting the instability of the retainer by using the trained KELM model. A KELM model is trained through the instability, obtained through calculation of an angular contact ball bearing dynamicmodel ADORE, of a retainer. and if not, the stability of the retainer under the action of other rotating speeds and loads can be predicted by adopting the trained KELM model, and calculation does notneed to be carried out by adopting a dynamic model, so that the calculation time is saved, and the analysis efficiency is improved.