53 results about "Autologistic regression" patented technology

The invention discloses a method and a system for predicting a flow of a self-adaptive differential auto-regression moving average model. The method and the system are used for causing a model to be more fit with a data trend of a present flow. The technical scheme comprises the following steps: utilizing an ARIMA (Autoregressive Integrated Moving Average) model to forecast the flow, and alarming when a practical value is deviated from a predicted confidence interval; while alarming, starting an alternative plan to monitor a flow data, for preventing an abnormal data from entering into ARIMA model prediction; and when the ARIMA model normally runs, judging if a parameter of the ARIMA model is still suitable in real time, and if not, automatically relearning and acquiring a new model parameter by relearning, thereby promoting the accuracy for model prediction.

The present invention proposes an autoregressive model channel prediction method based on quantum computing, including: step 1, obtaining known channel values, setting the order of the AR model, and predicting the channel value at the next moment according to the current known channel value; step 2 : Use the least squares method to solve the estimated value of the AR model coefficient to obtain the calculation formula; Step 3: Use quantum computing to solve the calculation formula to obtain the AR model coefficient |d> of the quantum state; Step 4: Define a set of measurement operators M ,via caculation <d|m|d>Obtain AR model coefficient estimates. The invention combines the least square estimation for solving AR model coefficients with quantum calculation, can effectively reduce the complexity of classical calculation, and the calculation result is equivalent to the existing coefficient estimation effect.< / d|m|d>

The invention discloses a crawler scheduling method, which is applied in the technical field of computers, and includes: obtaining data parameters of webpages to be crawled, and calculating statistics of the data parameters according to time series, and based on the statistics, the statistics include the number of times , mean, variance, covariance, and autoregressive coefficients. Through the logistic regression algorithm and the FTRL algorithm, determine the scheduling time for crawling the data parameters of the web page next time, and update the scheduling task queue according to the scheduling time. The invention also discloses a crawler scheduling device, electronic equipment and a storage medium, which can improve crawling efficiency of the crawler.

The invention discloses a dynamic process monitoring method based on a BP neural network auto-regression model, aims at establishing a nonlinear auto-regression model by using a BP neural network andimplement dynamic process monitoring on this basis. The main core of the method of the invention firstly lies in that the BP neural network is utilized to identify an autocorrelation model of sampleddata of a monitored object, and secondly lies in that an error after filtering by the BP neural network auto-regression model is utilized to implement process monitoring. The method provided by the invention has the main advantages that firstly, a nonlinear auto-regression model is established by utilizing a nonlinear fitting capability of the BP neural network, thereby achieving the purpose of removing nonlinear autocorrelation characteristics in measurement variables; and secondly, the method provided by the invention utilizes a capability of the error of reflecting the abnormal change condition of the nonlinear autocorrelation characteristics, and since autocorrelation no longer exists in error data, convenience is provided for subsequent establishment of a fault detection model. Thus,the method provided by the invention is more suitable for dynamic process monitoring.

The invention discloses a method for quantitatively calculating complex spectral component concentration. The method comprises the following steps of: acquiring an absorption spectrum of a pure substance to obtain different characteristic peaks of the pure substance and correlation functions among the different characteristic peaks; collecting spectral data of a to-be-detected sample to obtain anoriginal spectrum; carrying out qualitative analysis on the to-be-detected sample, and screening out the corresponding characteristic peaks and characteristic peak correlation functions of the pure substance; and importing all the screened pure substance and the characteristic peak correlation functions of the pure substance into the original spectrum, and performing learning and autoregression through adopting a deep learning algorithm to obtain the component concentration. According to the method, different characteristic peaks of the single pure substance are associated, so that the calculation speed is effectively increased; deep learning algorithm regression can be carried out on all characteristic peak concentrations of different components on the spectrum at the same time, so that the calculation speed and accuracy are improved; and a deep learning algorithm is adopted for concentration regression analysis instead of substance identification, the calculation speed is further increased, and calculation data can be output in real time.