31 results about "Recursive model" patented technology

Methods and modules for using physiological (biometric) variables to advance the state of the artificial pancreas. The method and system includes one or more modules for recursive model identification, hypoglycemia early alert and alarm, adaptive control, hyperglycemia early alert and alarm, plasma insulin concentration estimation, assessment of physical activity (e.g., presence, type, duration, expected effects on insulin sensitivity and GC), detection of acute stress and assessment of its impact on insulin sensitivity, detection of sleep and its stages and assessment of sleep stages on GC, sensor fault detection and diagnosis, software and controller performance evaluation and adjustment and/or pump fault detection and diagnosis.

The invention discloses a multi-state system dynamic reliability assessment method. According to state information monitored by multi-state systems and constitution logic of units in the multi-state systems, current state probabilities of the units in the multi-state systems are calculated by building a Bayes recursive model, and therefore the state probabilities and reliability of the multi-state systems in the surplus service period are deduced. The method makes full use of the state information monitored by each system to update a reliability assessment model. Compared with an existing reliability assessment method, the multi-state system dynamic reliability assessment method can achieve the fact that more accurate and dynamically updated reliability assessment values of each system can be obtained, and therefore each system can be effectively prevented from losing efficacy, and more accurate maintaining strategies are generated in a guided mode.

The invention discloses an adaptive unscented Kalman particle filtering method. The method utilizes the theory of the Sage filtering windowing method, also combines the idea of fading, estimates a true covariance matrix of the observed quantity by collecting an epoch innovation vector, and compares the true covariance matrix with the covariance matrix of a filtering recursive model, when a deviation exists between the two covariances, the observed covariance matrix of the system is adaptively adjusted according to the difference. Based on the process, an adaptive fading factor is designed, theobservation noise is further modified, the modified observation noise participates in the solution of a gain matrix, and thus the state estimation can be adaptively adjusted. The scheme of the invention can effectively perform filtering correction on a strongly nonlinear satellite/inertial integrated navigation system, especially when the external noise is abnormal, the filtering gain can be effectively and adaptively adjusted to improve the robustness and positioning accuracy of the system.

The invention discloses a gesture identification method based on a recursive model. The method comprises the basic steps of: 1, carrying out preprocessing on static and dynamic gesture images; 2, extracting static and dynamic gesture space sequences; 3, according to the gesture space sequences, constructing a gesture recursive model; and 4, by the gesture recursive model, carrying out gesture classification. According to the gesture identification method disclosed by the invention, in a form of converting the gesture space sequences into the recursive model, problems caused by different lengths of acquired gesture space sequences and incomparability of data values of sequence points are effectively solved, and robustness of a gesture identification algorithm is improved.

The invention discloses a reliability degree assessment method for multilevel state monitoring data fusion. The reliability degree assessment method specifically comprises the following steps of: determining states in a system and unit degradation process according to system and unit degradation laws, determining a unit state combination corresponding to the various states of a system, and collecting the state monitoring data or information of the system and units during a service process; updating the current state probability of the units in the system according to multilevel state monitoring data or information; dynamically estimating the reliability of the multi-state system in the remainder service period. According to the method disclosed by the invention, the state monitoring data or information of a system level and a unit level in the multi-state system is fused, the logical combination relationship between the system and unit degradation laws is combined, and the current state probability of each unit in the multi-state system is determined by constructing a Bayes recursive model, thus predicating the future state and reliability of the system; meanwhile, the errors of the state monitoring data or information are considered, thus the method disclosed by the invention is higher in universality.

The invention discloses an industrial data driving prediction control method based on subspace identification. The industrial data driving prediction control method includes steps of (1) calculating the predication model output and obtaining the predication output expressed by the predication increment according to the subspace model identification; (2) for the nonlinear time-varying characteristic, adopting the adaptive prediction control method of an online recursion identification model and updating the predication error of the before-after predication output and the process output through comparison to determine whether the predication model needs to be updated; and 3) performing constraint processing and for the physical constraints in the system, solving the problem by means of standard secondary programming and reducing the solution computational complexity by means of Lagrange daily functions. The industrial data driving prediction control method has the advantages of good practicality, high control precision, and simplified calculation.

The invention discloses an AUV cooperative navigation method of maximum correntropy adaptive cubature particle filter (MCACPF). The MCACPF is used for completing the state estimation problem in the AUV cooperative navigation process. During AUV cooperative navigation, a state equation and a measurement equation of the AUV cooperative navigation are reconstructed into a nonlinear recursive model, and then maximum correntropy cubature Kalman filter (MCCKF) is adopted in the framework of cubature particle filter (CPF) to generate an importance probability density function required in particle filter (PF), generated particles are then re-sampled by using a Kullback-Leibler distance (KLD) resampling method, and finally the estimation of an AUV state is obtained according to the algorithm flow of the CPF so as to realize the positioning of an AUV and complete the cooperative navigation. The MCACPF method is applied to the AUV cooperative navigation with outliers in measurement noise, higheraccuracy than existing PF, improved PF and robust filtering is obtained, and the computational complexity is lower than an existing improved particle filtering algorithm.

The invention relates to a budget estimate planning method and the technical field of networks, in particular to a power construction budget estimate planning method and a networking system thereof, realized by a computer system. The networking system comprises a design system inner network and budget estimate system software and is connected with an external network by a firewall and a gateway; the system software also comprises a system main module and a planning module (1), a resource management module (2), a sample database management module (3), an approval module (4), an information statistics and issue module (5) and a system interface and management module (6). The method adopts a tree structure recursive model to solve the problems of the online dynamic regulation and the traversal of a budget tree structure, adopts a multiplex iterative algorithm to solve the convergence problem of an aggregate project amount and adopts a Web Service program to solve the problems of data transmission and information share between distributed real-time heterogeneous databases. The invention already finishes 20 thousand of budget estimate planning and relates to construction amount amount over ten billion.

The invention discloses a method for identifying a combustion model of a circulating fluidized bed on the basis of least squares. The method is characterized in that a method of parameter estimation of basic least squares is used for the control method. The method specifically includes the steps that (a) the model is built; (b) an initial value is set, i.e., parameter estimation is built for the combustion process of the circulating fluidized bed, and the initial value of an algorithm is set; (c) data are sampled, i.e., the data of the fuel quantity (input) and steam pressure (output) provided for a boiler in the combustion process of the circulating fluidized bed are input; (d) parameter recursive estimation is conducted; and (e) iteration convergence is conducted. Through the estimation method based on the least squares, the characteristics of a large time delay, nonlinearity and strong coupling of combustion of the boiler are considered sufficiently, the recursive model is built, and real-time control and on-line correction of a computer are facilitated; therefore, the precision of the built model is greatly improved, and input and output models of other industrial objects are easily built while the reaction speed and position control precision of a process system are increased effectively.

The invention discloses a comprehensive energy system optimization operation method and system based on a model prediction control architecture. The method comprises the steps of obtaining source loadhistorical data; carrying out multi-step prediction on the source load historical data by adopting a recursive ARIMA model to obtain source load prediction data; obtaining a predicted value of the error through a grey prediction model according to the error between the measured data and the predicted data, and correcting the predicted value of the source load by using the predicted value of the error; and inputting the corrected source load prediction value into a rolling optimization model, and optimizing and outputting the prediction value of the output of each device by adopting a geneticalgorithm. In a feedback correction link, aiming at time lag of loads such as cold and heat, equipment cannot compensate prediction errors in a short time in a real-time adjustment stage, error multi-step prediction is introduced, and errors in a prediction link are compensated in advance, so that the equipment output is adjusted in advance, and the influence of fluctuation of renewable energy andload prediction on the operation of the system is reduced more effectively.

The invention discloses a freight flight gate allocation method, device and equipment and a storage medium. The freight flight gate allocation method comprises the steps of determining input information of a recursive model according to freight flight information and gate information; according to the input information, the recursion model outputs the corresponding relation between the flights meeting the constraint condition and the gate positions with the recursion frequency equal to the number of the flights needing to be allocated with the gate positions as the end condition; and allocating flight gate positions according to the corresponding relationship. According to the technical scheme provided by the embodiment of the invention, the problem of airport gate position allocation canbe solved through a recursive function-based backtracking algorithm.

The invention discloses a method for predicting the critical micelle concentration of a surfactant based on an ab initio model. The method comprises the following steps: obtaining a recursive model according to statistical thermodynamics of chemical potential; after the occupied volume and the chemical sites of micelles with different sizes are obtained through molecular dynamics simulation calculation, adopting the recursion model to express the concentration of the (n + 1) polymer into an analytical function of the concentrations of the monomer and the n polymer; ff the monomer concentration is used as a starting point, sequentially calculating the concentrations of n polymers with different sizes by using the recursive equation, wherein n is equal to 2, 3,..., so that the size of the CMC is deduced. The method provided by the invention can be applied to various types of surfactants.

An Agent-based solar sail distributed simulation system comprises an orbit recursive model unit, an earth shadow model unit, a thermal control system model unit, a power supply system model unit, an attitude control model unit and an orbit control model unit, each model unit is an independent Agent module, and interconnection and data interaction between the Agent modules are achieved through the TCP/IP protocol. According to the system, microscopic behaviors of each component system, namely on-orbit status of each subsystem of a solar sail, can be described, the Agent modules can also be combined to describe macroscopic characteristics of the solar sail, so that each component unit and the integral body of the solar sail can be fully simulated conveniently, and the problems in the design stage of the solar sail can be found timely. In addition, distributed design is adopted by the system, complexity can be reduced, and simulation efficiency of the system can be largely improved.