12 results about "Mean square difference" patented technology

The invention discloses a multivariable time sequence prediction method and system based on an implicit neural network. The method comprises the following steps: 1) collecting data and preprocessing the data; 2) performing window division on the standardized or normalized multivariable time sequence and determining the length of a to-be-predicted window; 3) performing variable correlation coding on the input window to obtain a variable-level feature vector; 4) the implicit neural network based on time attention predicts target parameters by using the variable features in the step 3), and implicit neural representation of the target sequence is modeled through the parameters; 5) taking the output of the implicit nerve representation and the original input window as the input of the multi-head attention predictor, and obtaining a prediction result through cross-sequence cross attention calculation performed in the implicit space and multi-layer perceptron conversion output dimension; and 6) training and optimizing model parameters, calculating a mean square error of a prediction result and a real result, taking the mean square error as a loss function, carrying out back propagation to optimize trainable parameters of the variable correlation coding module, the implicit neural network module multi-head attention predictor and the multi-layer perceptron, and then repeating the steps 3) to 6) to obtain the multi-head attention predictor. Until the preset number of iterations is reached or the error of the model on the verification set meets the requirement of early stop; and 7) performing prediction by using a model of training convergence, and performing reverse normalization on a prediction result to obtain a final prediction result. The method has good generalization, and meanwhile, the interpretability of the attention mechanism is remarkably improved by generating the hidden space characteristics of the trend component and the season component.

The invention belongs to the field of water-wind-light multi-energy complementary optimal scheduling, and particularly discloses a water-wind-light optimal scheduling method and system based on a two-stage dual-population evolutionary algorithm, and the method comprises the steps: taking the water level of a reservoir as a decision variable, taking the maximum power generation amount and the minimum residual load mean square deviation as objective functions, and constructing a multi-objective scheduling model; the two populations are initialized, and the water-wind-light multi-target scheduling model is solved; the two populations are initially in a fixed division stage, and operators A and B are respectively adopted for iterative updating; when a switching condition is met, switching to a self-adaptive cooperation stage for iterative updating, determining the selection probability of the operators A and B according to population performance at the moment, and selecting the operators based on the selection probability for iterative updating; the switching condition is that after the population is iteratively updated each time, if the population diversity is smaller than a threshold value or the evaluation frequency reaches the threshold value, stage switching is carried out. According to the method, the contradiction between convergence and diversity in water-wind-light multi-objective optimization can be solved, and accurate optimization is realized.

The invention relates to the technical field of monitoring and early warning, in particular to a non-coal mine slope monitoring and early warning equipment threshold setting method based on an AI algorithm, and the method comprises the following steps: obtaining the change trend of a slip rate through introducing time sequence derivative analysis, and screening an abnormal turning feature section; the method comprises the following steps of: establishing a mapping relation between curvature and grade by combining a current equipment grade boundary parameter, constructing a curvature threshold interval adaptive to equipment state change, fusing and training a neural network model by utilizing Euclidean distance of a plurality of disturbance factor groups and mean square error characteristics in a time window, performing accurate classification on disturbance density grades, and calculating the disturbance density. The calculation of the level correction weight is completed through the proportional mapping between the disturbance level and the preset level, and the weight correction is performed on the initial level threshold parameter, so that each equipment threshold has the self-adaptive adjustment capability for the current state, and the dynamic response capability of the early warning threshold setting and the anomaly recognition accuracy are further improved.

The invention provides a music time coding method for artificial neural network modeling. The method aims at the coding requirement of music beat time, molecules are decomposed according to a fixed denominator for multi-bit independent classification coding, and the method comprises the following steps: setting the fixed denominator as a composite number and decomposing the composite number into a prime factor combination; the molecules are mapped into vectors, the value range of each bit is determined by a cardinal number of a prime factor combination, and the vectors are converted into one-hot coding sub-vectors with redundant components removed. The technical scheme of the invention solves the problem that the traditional mean square error (MSE) ignores the internal correlation of scores and has high sparsity in the topology recognition task of an optical music recognition system (OMR), and compared with a traditional coding method adopting floating point numerical output and a mean square error loss function under the conditions of the same data set, model architecture and training hyper-parameters, the method provided by the invention has the advantages that the coding efficiency is greatly improved; according to the method, the comprehensive error index is reduced by about 21%, and the model precision and the training efficiency are improved.

This disclosure relates to the field of bridge engineering, specifically to a design method, system, and medium for HPC-RC combined eccentrically compressed columns. The method includes: defining a design space; selecting optimal design data combinations based on engineering specification constraints, bearing capacity constraints, and cost-effectiveness values ​​to construct a design data sample set; constructing a two-branch heterogeneous neural network model, the model including an input layer, a shared feature extraction layer, a diameter prediction classification branch, and a reinforcement area prediction regression branch; constructing a loss function composed of focal loss and mean square error loss; training the two-branch heterogeneous neural network model using the design data sample set; and using the trained two-branch heterogeneous neural network model to predict the diameter and total reinforcement area of ​​the eccentrically compressed column. This disclosure improves the stability and accuracy of the design, reduces the computational burden, increases computational speed, and reduces computational complexity.

According to the method for measuring the surface type parameters of the transparent thin plate, outlier identification and correction are carried out on collected surface coordinates and thickness data of the transparent thin plate before and after polishing through an abnormal value detection algorithm and adjacent non-abnormal point linear interpolation replacement, and then noise reduction processing is carried out on the corrected data by adopting a dynamic noise reduction combination. The method comprises the following steps of: acquiring surface coordinates and thickness data of a transparent thin plate, performing interpolation prediction on the unacquired surface coordinates and thickness data of the transparent thin plate, finally performing data fitting, generating a thickness thermodynamic diagram, calculating the bending degree, the warping degree, the total thickness change and the local thickness change of the transparent thin plate, simultaneously generating a removal quantity thermodynamic diagram, calculating the kurtosis, the skewness, the mean square error and the local deviation value, and judging the polishing uniformity; the method is high in key defect recognition accuracy, high in interpolation adaptability and high in parameter acquisition precision, multi-dimensional quantitative analysis of the polishing uniformity of the transparent thin plate is achieved by introducing indexes such as kurtosis, skewness, mean square deviation and local deviation, and data support is provided for technological parameter adjustment.

The invention discloses a storage and calculation integrated chip-based neural network quantification method and device. The method comprises the following steps of performing dimension expansion processing on an original base library vector and an original target vector; based on the mean value and the mean square error of each dimension of the dimension expansion base library vector, normalizing the dimension expansion base library vector and the dimension expansion target vector; sorting data of each dimension of the normalized base library vector to obtain two identification points in a sorting result of each dimension, determining a base library data core distribution interval corresponding to the dimension based on the identification points, and taking the base library data core distribution interval as a reference to obtain a base library data core distribution interval corresponding to the dimension; mapping data of a dimension corresponding to a bottom library data core distribution interval in the normalized bottom library vector to a first hardware adaptive quantization interval; and determining a corresponding target data core distribution interval according to the first number, the second number, the maximum value and the minimum value in the normalized target vector, and mapping the normalized target vector to a second hardware adaptive quantization interval by taking the target data core distribution interval as a reference.

The invention discloses a precipitation prediction method based on a GRU and an LSTM neural network. The precipitation prediction method comprises the following steps: selecting meteorological station data continuously monitored for a long time; removing abnormal values of the meteorological station data, and complementing missing values; selecting first 85% of historical data for model parameter training, and selecting last 15% of recent data for model generalization ability test; normalizing the three precipitation index dimensions of the training set and the test set respectively; combining an algorithm model by using GRU and LSTM components; inputting the preprocessed training set data into the network, using a mean square error as a loss function, adopting an Adam algorithm to optimize the loss function, and iteratively updating network parameters until the model converges; data to be predicted are input into the trained model after being preprocessed in the first step, and a precipitation prediction result is output. Sequence data information is processed through multiple influence factor data and the superposed GRU and LSTM combination, the gradient disappearance problem is avoided, the prediction effect is good, precision is high, and applicability is good.

The application discloses a source-load power prediction method and device considering different granularities and a storage medium. The method comprises the following steps: collecting source-load historical data in a to-be-predicted area; pre-processing the source-load historical data to obtain multiple groups of data groups with different granularities; inputting the multiple groups of pre-processed data groups with different granularities into a pre-constructed and trained MultiGNet prediction model respectively to obtain a prediction result of source-load bilateral power; wherein the MultiGNet prediction model is obtained by extracting semantic dependency features of different granularity data groups and establishing a connection between different granularity data groups through a cross-granularity learning module, analyzing feature relationships of different granularity data groups through a granularity attention module to obtain optimal weights of different granularity data groups, and training source-load historical data with a minimum mean square error loss function as a target. The application fully utilizes rich information of different granularity data for prediction, and improves the accuracy of source-load power prediction.

The application belongs to the field of water, wind and light multi-ability complementary optimal scheduling, and particularly discloses a water, wind and light optimal scheduling method and system based on a two-stage double-population evolution algorithm, which comprises the following steps: constructing a multi-objective scheduling model by taking reservoir water level as a decision variable and taking maximum power generation and minimum residual load mean square deviation as objective functions; initializing two populations and solving the water, wind and light multi-objective scheduling model; the two populations are in a fixed division stage at the beginning and are respectively updated by operators A and B; when the switching condition is met, the two populations are switched to an adaptive cooperation stage for iterative updating, at this time, the selection probability of operators A and B is determined according to the population performance, and the operators are selected based on the selection probability for iterative updating; the switching condition is that the population diversity is less than a threshold value or the evaluation number reaches a threshold value after each iterative updating of the population, and the stage is switched. The application can solve the contradiction between convergence and diversity in water, wind and light multi-objective optimization and realize precise optimization.

The application discloses a kind of centroid extraction method and extraction system of autocollimator image, wherein based on the Gaussian filter of mean square deviation σ is filtered to image;According to the edge of the image refined by multistage calculation, the edge detection of image is completed, and edge pixel point is determined;Ellipse centroid is fitted to edge pixel point;Based on each ellipse centroid is normalized, and the centroid of image is calculated, at this time, the edge of the image refined by filtering processing under multistage calculation, and corresponding edge pixel point is determined, ellipse centroid is fitted to edge pixel point, and based on each ellipse centroid is normalized, by this method, the influence of unstable gray on centroid extraction can be overcome, and the centroid extraction precision of autocollimator image is improved.