The invention discloses a device life prediction method based on multiple long/short-term memory network and experience bayesian, which belongs to the technical field of mechanical equipment state monitoring and life prediction. The method comprises the following steps: 1) a plurality of monitoring signals of the mechanical equipment are obtained, and the collected monitoring signals are preprocessed; 2) the monitoring signals are screened, and signals which can well reflect the degradation process of the mechanical equipment can be selected; 3) a plurality of long-term memory network models are constructed to realize multiple network synchronous training; 4) the signals collected by the sensors in real time are input into the multiple-length short-term memory network model, a plurality ofnetworks are subjected to parallel prediction, and a prediction result is obtained; 5) the probability distribution of the prediction result is estimated through an empirical bayesian algorithm, andthe most probable service life of the equipment is deduced. According to the invention, the remaining service life of the mechanical equipment can be accurately predicted in real time, so that the faults of the mechanical equipment can be sensed in advance, the safety, stability and long-period running of the mechanical equipment can be guaranteed.

Device life prediction method based on multiple long/short-term memory network and experience bayesian

The invention relates to an intrusion detection method based on an extreme learning machine. The method comprises: step one, preprocessing is carried out on data in a network flow data set; to be specific, classified marking is carried out on training data by using character type feature values as numerical value type feature values in a testing set and a training set; and normalization and standardization processing is carried out on the training set and the testing set; step two, according to class tags, the network flow training data are classified into Normal, DOS, Probe, U2R, and R2L classes, wherein the Normal, U2R, and R2L classes are combined into an Other class; step three, a first layer ELM model is trained by using DOS, Probe and Other training data, wherein the ELM model employs a PReLU activation function to replace a Sigmoid activation function for improvement; step four, improvement is carried out by using a PReLU activation function to replace a Sigmoid activation function; and step five, detection classification is carried out.

Intrusion detection method based on extreme learning machine

The invention relates to a cancer recurrence prediction system based on a multi-dimensional Gaussian distribution Bayesian classification, which comprises a preprocessing module, a training module anda Bayesian classifier; the pre-processing module performs data cleaning on the training set and generates a class vector data set; the training module first calculates the first probability of two class attributes, and then divides the data attribute into a class data attribute set which is closely related to the class attribute and a class II data attribute set which is sparse with the class attribute association degree by using the pearson correlation coefficient, two types of data attribute sets are respectively used for calculating a corresponding probability by using a multi-dimensionalGaussian distribution and a one-dimensional Gaussian distribution; the Bayesian classifier combines both the probability of the two and the first probability of the class together as the probability of the data belonging to each class, and the classification test result of the cancer is judged accordingly. The cancer recurrence prediction system based on multi-dimensional Gaussian distribution Bayesian classification improves the predictive accuracy of the recurrence of the cancer.

Cancer recurrence prediction system based on multi-dimensional Gaussian distribution Bayesian classification

The invention discloses a strip steel exit thickness prediction method by a symmetric extreme learning machine (Sym-ELM) optimized by a random frog leaping algorithm. Symmetry prior information is firstly added to the traditional extreme learning machine, the improved extreme learning machine can process a class of data containing symmetric properties, a symmetric extreme learning machine is put forward, the improved Sym-ELM algorithm is further optimized, the random frog leaping algorithm is used to select the input weight and the hidden layer bias value of the Sym-ELM algorithm, the optimized extreme learning machine is used for strip steel exit thickness prediction, and optimization on the symmetric extreme learning machine algorithm not only minimizes a root mean square error value but also considers a normal condition number of the output matrix of the hidden layer. The experiment shows that in comparison with the traditional extreme learning, the prediction performance of the traditional extreme learning machine to process the strip steel data containing the symmetric properties is improved, prediction errors of the model are reduced, and the generalization performance of the model is improved.

Strip steel exit thickness prediction method by symmetric extreme learning machine (Sym-ELM) optimized by random frog leaping algorithm

The invention provides a blast furnace molten iron silicon content change trend prediction method, a blast furnace molten iron silicon content change trend prediction device and a storage medium. Themethod includes the step that characteristic parameters during an iron making process which are related to silicon content change in blast furnace molten iron are acquired, and a silicon content change trend is predicted on the basis of trained blast furnace molten iron silicon content prediction models, wherein the blast furnace molten iron silicon content prediction models include a first layerprediction model and a second layer prediction model, wherein the first layer prediction model is used for preliminarily predicting the silicon content change trend according to an original sample, and the second layer prediction model predicts the silicon content change trend for the second time according to the prediction result of the first layer prediction model so as to obtain the silicon content change trend. According to the blast furnace molten iron silicon content change trend prediction method, the blast furnace molten iron silicon content change trend prediction device and the storage medium of the invention, a limit gradient enhancement and long-term and short-term memory network fusion model is established to predict the trend of the silicon content in the molten iron, a reference basis is provided for a blast furnace operator to judge the change trend of the condition of the blast furnace and control the amplitude of the blast furnace in advance, and therefore, the smoothness of the iron making process can be ensured, and the quality of the molten iron can be maintained within a normal range.

Blast furnace molten iron silicon content change trend prediction method, device and storage medium

The invention discloses an interval type index forecasting method based on a Bayesian network and an extreme learning machine, belongs to the fields of automatic control, information technologies and advanced manufacturing, and particularly relates to learning of parameters of an asymmetric Gaussian distribution Bayesian ELM model and adaptive adjustment on asymmetric weights. The interval type index forecasting method is characterized by including the following steps: as for the characteristic of the uncertainty of a complex production process, production indexes are described through the number of intervals, asymmetric Gaussian distribution serves as output distribution in an ELM model, the Bayesian ELM model with the weights is obtained, and the parameters of the Bayesian ELM model are learnt under an experience Bayesian frame through practical running data in the complex production process; on the basis, the pair of reciprocal weights are learnt with an adaptive adjustment method, and finally the forecasting value of the interval type indexes is obtained. By means of the interval type index forecasting method, the production indexes in the practical production process can be forecast, and the interval type index forecasting method can be used for guiding operation optimization and dynamic scheduling in the production process.

Interval type indicator forecasting method based on bayesian network and extreme learning machine

The invention discloses an interval type index forecasting method based on a robust interval extreme learning machine, belongs to the fields of automatic control, information technologies and advanced manufacturing, and particularly provides an interval type index forecasting method for achieving production index forecasting under the conditions that as for the production process in the uncertain environment, data comprise abnormal points, and indexes can be described through the number of intervals The interval type index forecasting method is characterized by including the following steps: an interval upper-bound model and an interval lower-bound model are built through the interval extreme learning machine, and an optimization problem is built to optimize parameters of the two models, wherein an objective function of the optimization problem considers the model complexity, the model center error and the model interval error In order to reduce influences of the abnormal points on the index forecasting performance, the abnormal points in an initial training data set is processed with a least median square method in robust statistics to determine sub-training-data-sets The interval type index forecasting method can be used for forecasting the interval type production indexes with the abnormal points contained in the data

Interval type index forecasting method based on robust interval extreme learning machine

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Interval type indicator forecasting method based on bayesian network and extreme learning machine

Interval type index forecasting method based on robust interval extreme learning machine