13 results about "Maximum entropy method" patented technology

The invention relates to a low-altitude Internet of Things dynamic spectrum allocation and access method and system based on deep reinforcement learning. The method comprises the following steps: S1, constructing an unmanned aerial vehicle network of the low-altitude Internet of Things; s2, each unmanned aerial vehicle carries an act-critic network, a maximum entropy method is introduced into the act-critic network for optimization, and an MAEAC reinforcement learning model is formed; and S3, training the MAEAC reinforcement learning model to obtain a trained MAEAC reinforcement learning model, and obtaining an optimal strategy of spectrum allocation and access. The invention provides a low-altitude Internet of Things dynamic spectrum allocation and access method and system based on deep reinforcement learning, and the algorithm improves the randomness of a strategy and accelerates the convergence speed through adding the updating of an entropy item execution strategy. The problem of how to realize stable communication of the unmanned aerial vehicle network under limited spectrum resources in the low-altitude Internet of Things is solved, and a good effect is achieved.

The present invention discloses a maximum a posteriori ship wave load reliability analysis method of an improved maximum entropy method, which belongs to the technical field of ship wave load response and reliability assessment, including obtaining a function function of a ship profile load response, and obtaining variables and their distribution information that affect the function function of the ship profile load response; initially extracting a small number of samples based on a Sobol sequence in a sampling space to form a training set, and obtaining function values ​​according to the training set; on the basis of the training set, using a Gaussian random process, a neural network and other proxy model methods to construct a proxy model. The present invention uses the maximum a posteriori and proxy model methods, uses the proxy model method to obtain the mapping relationship of the function function, and uses the mapping relationship as a priori information based on the maximum a posteriori framework, effectively alleviating the problem that the prediction accuracy of the traditional integer-order moment maximum entropy method is not high due to insufficient information, thereby reducing the amount of function function calculation and improving the calculation accuracy.

The invention provides a reinforcement learning method skilled at multiple targets and multiple tasks, which comprises the steps of constructing a deep reinforcement learning framework comprising a strategy network and an evaluation network, introducing a candidate set expansion and offset coefficient weighting mechanism in an action sampling stage, and performing multi-target and multi-task evaluation on the basis of probability distribution of multiple groups of parameterization in the strategy network; diversified candidate solutions are formed in the action space; a cosine similarity regular term is introduced into the loss function so as to maintain the difference between different action candidates; the evaluation network adopts a double-Q network, and random disturbance is applied to a Q branch, so that the over-estimation problem is relieved, and the value evaluation robustness is improved. The interaction experience of the intelligent agent and the environment supports coexistence of various experiences, including a master experience of a strategy network through normal distribution sampling, a slave experience through Cauchy distribution sampling and a random experience of random sampling. Compared with a traditional maximum entropy method, the method shows better exploration efficiency, strategy generalization performance and task migration ability in a complex high-dimensional task environment.

The present application belongs to the field of software analysis, and specifically discloses a Bayesian verification method, system and device for reliability of discrete software, the method comprising: obtaining a discrete data set for reliability growth testing of discrete software; determining a reliability growth model of discrete software by combining the data set and a non-homogeneous Poisson process in a discrete time domain; estimating the probability of discrete software successfully executing a preset specified task again based on the reliability growth model; setting the prior distribution of reliability to a beta distribution, and estimating the parameters of the beta distribution based on the probability of successfully executing a preset specified task and the maximum entropy method; determining the posterior distribution of reliability based on the prior distribution and Bayes' theorem; determining the number of test cases required for reliability verification of discrete software under a preset tolerable number of failures based on the posterior distribution; and performing reliability verification based on the number of test cases and the tolerable number of failures. Through the present application, an effective reliability verification scheme for discrete software is proposed.

The invention provides a wind speed simulation method based on a time-varying autoregression model and a maximum entropy method. The wind speed simulation method based on the time-varying autoregression model and the maximum entropy method comprises the following steps: S1, determining a non-stationary characteristic of a target wind speed through a specified evolution power spectral density, namely an EPSD function, calculating a pulse matrix of a time-varying autoregression, namely a TVAR model, and considering a single-variable non-Gaussian non-stationary wind speed process x (t); the mean value and the standard deviation of the time change are respectively expressed as mu x (t) and sigma x (t), and t represents time. By adopting the wind speed simulation method combining the time-varying autoregression model and the maximum entropy method, the coexistence problem of non-Gaussian property and non-stationarity of special wind speeds such as typhoon and thunderstorm is effectively solved, and the non-stationarity characteristic in the wind speed process is captured through the time-varying autoregression model, so that the simulation is more accurate.

The invention discloses an intake tower anti-seismic reliability maximum entropy calculation method based on a dynamic integral boundary. The method comprises the following steps: firstly, determining a plurality of anti-seismic performance levels of an intake tower and corresponding tower top displacement thresholds; taking the seismic oscillation peak acceleration as a random variable, and generating sample points by using a layered Latin hypercube sampling method; adopting a C-P power spectrum model to generate a random seismic wave sample; obtaining a structure response value through finite element dynamic analysis, and constructing a performance function, thereby calculating first four-order statistical moments of the performance function; dynamically determining an integral boundary for each performance level; and by taking the statistical moment as a constraint, fitting a probability density function of the performance function by applying a maximum entropy method in a dynamic boundary, and carrying out integral calculation on the probability density function to obtain a failure probability and a reliable index. Thus, the problem that the fixed integral boundary and the performance level of a traditional maximum entropy method are disjointed is solved, the calculation efficiency is guaranteed, and meanwhile an accurate quantification means is provided for evaluating the anti-seismic reliability of the water inlet tower.

This invention discloses a maximum entropy reinforcement learning method based on covariance parameterization, belonging to the fields of intelligent control and machine learning. This method achieves explicit control and automatic adjustment of policy entropy by parameterizing the covariance matrix of the policy distribution, thereby improving the policy's ability to explore the continuous action space. This invention further overcomes the limitation of traditional maximum entropy methods that rely solely on standard deviation methods, realizing a more advanced maximum entropy policy optimization method. This invention includes policy covariance parameterization and a positive definite covariance constraint mechanism. Experiments show that this invention is effective and performs better than existing methods in experimental environments. This invention is applicable to applications such as robot control and intelligent agent navigation.

The present invention discloses a product life prediction method based on zero-failure data in a sequential test in the field of open reliability test evaluation. According to the truncation time from small to large, the product is tested in sequence, and the Bayesian estimation model is used to obtain the Bayesian estimation value of the cumulative failure probability of each sample #imgabs0#. The set #imgabs1# is used as the input of the grey bootstrap model, t i 0 to the longest test duration t k At any time between , a self-service sample set is sampled to generate new grey self-service sample points, forming a set of cumulative failure rate estimation results at multiple times, constructing a square loss function, and obtaining the parameter estimation value that minimizes the square loss function. Using the maximum entropy method, the parameter estimation value with the largest information entropy corresponding to the grey self-service sample point among all parameter estimation values ​​is obtained as the final result, and the prediction result is accurate.

The present invention relates to the technical field of new energy vehicle collisions, and discloses an optimization method for modeling the uncertainty of a frontal collision of an electric bus. First, a finite element model of the frontal collision of the electric bus is established to obtain design variables. Based on the polynomial chaos expansion method, the relationship between the design variables and the responses is established. Combining the Sobol’ index method for sensitivity analysis, the design variables with high sensitivity are obtained. An uncertainty optimization mathematical model for the frontal collision of the electric bus is established, and it is solved by combining a multi-objective optimization algorithm. This method optimizes the frontal collision cross-section with a 100% overlap rate considering uncertainty for the front end of a certain electric bus. Taking the cross-sectional dimensions of the tube beam components as the optimization variables and the collision intrusion speed as the optimization objective, the uncertainty analysis and optimization design research of the electric bus are carried out by combining the polynomial chaos expansion method and the maximum entropy method, reducing the influence of uncertainty factors on the optimization results, and ensuring the robustness and reliability of the design scheme.

The invention provides a seed lotus variety identification method and system based on an unmanned aerial vehicle image, and relates to the technical field of seed lotus variety identification. The unmanned aerial vehicle image is preprocessed, the preprocessed image is segmented, a lotus leaf and a background are segmented through a maximum entropy method, and a seed lotus variety identification result is obtained. Dividing the lotus leaf into a mesophyll part and a texture part through a maximum between-class variance method, respectively performing feature extraction on the lotus leaf part and each part to obtain each feature parameter, analyzing and processing the extracted feature parameters to establish each seed lotus feature index, and respectively comparing the seed lotus feature index with a critical value to obtain a seed lotus feature index; and comprehensively analyzing the comparison result, and identifying the seed lotus variety.

This invention discloses a method and system for simulating stationary non-Gaussian wind pressure based on the maximum entropy method and moment transformation function. The method calculates the marginal probability density function (PDF) based on the k-th primitive moment of the target standard non-Gaussian wind pressure using the maximum entropy method; and uses the marginal probability density function (PDF) f Xj (x) Determine the transfer function; calculate the cross-correlation matrix based on the target cross-power spectral density matrix; divide the transformation relationship into region I and region II based on the error boundary line in the non-Gaussian correlation coefficient estimation; calculate the cross-correlation matrix ρ of the latent standard non-Gaussian vector for region I and region II respectively. G (τ) and the latent standard non-Gaussian vector cross power spectral density (PSD) matrix S G (ω); based on the latent Gaussian cross-power spectral density matrix S G (ω) Obtain Gaussian samples Z(t), and then obtain samples of non-Gaussian wind pressure vector X(t) based on the Gaussian samples Z(t). This method can provide better simulation accuracy, especially for wind pressure with strong non-Gaussian characteristics; since this method derives the Gaussian correlation function as a function of the non-Gaussian correlation function, it has higher simulation efficiency and better simulation accuracy.

The present invention discloses a soil or sediment attribute mapping method integrating multi-source data and uncertain information, and belongs to the field of soil sediment attribute prediction and mapping. The main steps are: classifying and sorting the acquired soil or sediment attribute data, distinguishing accurate soil attribute hard data and soil attribute soft data with uncertainty, wherein the soil attribute soft data can be derived from the transformation of historical data, model estimation of related soil sediment attributes, etc.; combining the Bayesian maximum entropy method framework, using the maximum entropy theorem to combine the soil attribute hard data and the soil attribute soft data to obtain the prior probability density function, and then searching for the soil attribute hard data and the soil attribute soft data around the prediction space point, and using the Bayesian theorem to update the prior probability density function, obtaining the posterior probability density function, and obtaining the predicted value and prediction uncertainty of the soil attribute by integrating the posterior probability density function.

The invention discloses a maximum posterior ship wave load reliability analysis method based on an improved maximum entropy method, and belongs to the technical field of ship wave load response and reliability evaluation.The method comprises the steps that a performance function # imgabs0 # of ship profile load response is obtained, and a variable # imgabs1 # influencing the performance function of the ship profile load response and distribution information of the variable # imgabs1 # are obtained; initially, a small number of samples # imgabs3 # are extracted from a sampling space # imgabs2 # based on a Sobol sequence to form a training set # imgabs4 #, and a function value # imgabs6 # is obtained according to the training set # imgabs5 #; on the basis of the training set # imgabs7 #, a proxy model # imgabs8 # is constructed by using a Gaussian random process, a neural network and other proxy model methods. According to the method, a maximum posteriori and proxy model method is applied, the mapping relation of the performance function is obtained by using the proxy model method, and the mapping relation is used as prior information based on a maximum posteriori framework, so that the problem of low prediction precision of a traditional integer moment maximum entropy method due to insufficient information is effectively relieved, the calculation amount of the performance function is reduced, and the prediction efficiency is improved. And the calculation precision is improved.