10 results about "Inverse system" patented technology

The invention relates to the technical field of CPU card chips, and discloses a self-adaptive power consumption control method and system of a CPU card chip. The method comprises the following steps: collecting power consumption characteristic data of a CPU card chip in different working load states, and constructing a power consumption signal model; creating a generalized power consumption control operator, and performing matrix decomposition on the power consumption characteristic data of the CPU card chip to obtain a power consumption weight vector; performing dual-state monitoring and feature selection on the power consumption state vector and the performance state vector which are acquired in real time to obtain a low-power-consumption sparse control matrix; performing parameter optimization training through a five-layer fuzzy neural network to obtain a target inverse system model; the target inverse system model and the original power consumption control system are connected in series to form a pseudo-linear system, a linear closed-loop controller is added, and the decoupling control system is obtained, compared with a traditional method, the decoupling control system has the advantages that the energy efficiency ratio of a CPU card chip is remarkably increased, and guarantee is provided for efficient operation in various working environments.

A design methodology and tool called INFORM are provided that use a two-phase approach for sample-efficient constrained multi-objective optimization of real-world nonlinear systems. In the first optional phase, one may modify a genetic algorithm (GA) to make the design process sample-efficient, and may inject candidate solutions into the GA population using inverse design methods. The inverse design techniques may be based on (i) a neural network verifier, (ii) a neural network, and (iii) a Gaussian mixture model. The candidate solutions for the next generation are thus a mix of those generated using crossover / mutation and solutions generated using inverse design. At the end of the first phase, one obtains a set of nondominated solutions. In the second phase, one chooses one or more solution(s) from the non-dominated solutions or another reference solution to further improve the objective function values using inverse design methods.

The invention provides a dynamic compensation method for a temperature-pressure composite sensor, which comprises the following steps of: acquiring amplitude-frequency and phase-frequency characteristic curves through frequency response test, determining bandwidth limitation and phase lag characteristics, and designing a digital compensation filter by adopting an inverse system method to improve amplitude flatness and phase linearity in a preset frequency band; meanwhile, linear subintervals are divided according to piecewise nonlinear characteristics, a dynamic compensation model is established, and gain and phase correction coefficients are extracted; and according to a measured value, switching subinterval models in real time and adjusting a filtering coefficient to ensure a dynamic tracking characteristic. According to the invention, the optimized frequency response characteristic is finally integrated to an output signal processing flow, and the measurement precision and response speed of the temperature and pressure sensor under complex working conditions are significantly improved.

The invention relates to a decoupling control method for an inverse system of a bearingless composite rotor cage type asynchronous motor. The bearing-free composite rotor cage type asynchronous motor inverse system decoupling control method comprises the following steps: S1, establishing an ISA-GA optimized SVM inverse system; s101, establishing an SVM (Support Vector Machine) model: on the reversible basis of the CCR-BIM system, constructing the SVM model to identify and fit an inverse system of the CCR-BIM system; s102, carrying out optimization design on an SVM kernel function based on ISA-GA; s2, an SVM inverse system decoupling control system based on ISA-GA optimization; according to the bearing-free composite rotor cage type asynchronous motor inverse system decoupling control method, through selection operator crossing and mutation operation in an improved SA-GA algorithm, the search efficiency and the local optimization capacity are improved, the inverse system and an original system are connected in series to form a pseudo-linear system, CCR-BIM linearization and decoupling control are achieved, and the decoupling precision is improved.

The invention discloses a power distribution network fault transient signal detection method and system based on inverse system inversion, and the method comprises the steps: obtaining a primary side current signal and a secondary side voltage signal based on a transient signal sensor at a power distribution network fault; establishing a transfer function of the nonlinear transmission model of the transient signal sensor of the power distribution network, and performing inverse transformation to obtain an inverse system transfer function of the transient signal sensor; discretizing the inverse system transfer function to obtain a difference equation of a discrete inverse system; and reading the secondary side voltage signal data satisfying the preset data length in the FIFO cache region by using a difference equation, and performing inversion processing to obtain data reflecting the primary side fault current signal characteristics. According to the method, the distortion problem caused by the fact that the primary side transient signal is converted into the secondary signal through the sensor is solved, the characteristic identification precision of the fault transient signal is improved, and the detection accuracy and reliability of the fault transient signal are improved.

The invention provides a compensation method for non-ideal characteristics in an analog signal amplification link, and relates to the technical field of weak signal detection. The method comprises the following steps: determining an equivalent low-pass filter circuit of the amplifying circuit; determining a transfer function of the equivalent low-pass filter circuit; determining an attenuation coefficient; determining an equivalent transfer function of the non-ideal characteristic of the amplification circuit at the phase-sensitive detection output end; performing inverse operation to obtain an inverse system transfer function; carrying out regular design on the inverse system transfer function; and designing an analog system or a digital system according to the inverse system transfer function of the regular design to compensate the non-ideal characteristics of the analog signal amplification link. According to the invention, the problem of signal distortion caused by non-ideal characteristics such as uneven amplitude-frequency response and phase nonlinearity in an analog signal amplification link is solved, and especially the technical problems of measurement precision reduction and measurement accuracy reduction caused by non-ideal frequency response characteristics of a detection system in a weak signal detection scene are solved.

The invention discloses a decoupling control method, system and device for a matrix motor inverse system and a medium. The decoupling control method comprises the following steps: acquiring a state parameter v of the current matrix motor inverse system; inputting the state parameter v of the current matrix motor inverse system into a trained inverse system model based on a deep neural network to obtain an action parameter u output by the inverse system model; according to the action parameter u output by the inverse system model, closed-loop control is conducted on the control matrix motor, decoupling control of the inverse system of the matrix motor can be achieved through the method, the system, the equipment and the medium, and reliability and accuracy are high.

The present invention discloses a TBCC engine multivariable performance control method based on neural network prediction feedback and inverse control, the control method includes: a multivariable performance parameter direct controller based on a neural network inverse system; a performance parameter estimator based on a BP neural network; using the multivariable performance intelligent controller, designing a certain type of TBCC engine closed-loop performance control system, and adding a PI compensator on this basis, online correction and update of controller parameters, and studying the tracking performance of the control system. The present invention uses a neural network combined with a derived TBCC multivariable control method, with the goal of minimizing the thrust flow change during the TBCC engine mode conversion process, designs a multivariable controller, calculates the engine control quantity, and controls the fuel flow, afterburner fuel flow and tail nozzle critical area. The control method has a simple structure and has a certain positive role in reducing the workload of the design controller and the design of the multivariable control law.

This invention discloses a deep learning-based method for EEG acquisition and analysis, comprising: Step 1: acquiring multi-channel EEG sequences, segmenting them according to time windows, and setting calibration time slots to form segments; Step 2: generating calibration sequences and writing them into the acquisition terminal register within the calibration time slots; Step 3: constructing an input labeled sequence and inputting it into an improved FNet model, outputting link transfer function parameters and contact state vectors; Step 4: performing inverse system reconstruction operations and applying stability constraints to form a reconstructed EEG sequence; Step 5: generating channel-time masks and performing suppression and interpolation to form a clean EEG sequence; Step 6: performing task analysis and processing to form task inference results and calculating a joint credibility index; Step 7: generating and issuing acquisition control command sets. This invention achieves online calibration, stability compensation, and closed-loop adaptive adjustment for EEG acquisition.

The application discloses a kind of unmanned plane angular motion control methods based on incremental local weighting, first based on inverse system idea, the local approximate inverse model of unmanned plane inner loop is constructed, and the control law of unmanned plane inner loop is obtained;Then based on local weighting learning method, the parameter adaptive updating law of unmanned plane inner loop is designed, and the regression parameter is updated, the real-time approximation of local approximate inverse model is realized;Then incremental local weighting method is used to design structure adaptive updating law, and new local approximate inverse model is automatically added when facing unknown characteristic data;Finally, the outer loop controller of the angular motion control of unmanned plane is designed, and the attitude angular velocity command is input to the unmanned plane inner loop;The application utilizes the method that local weighting learning and dynamic inverse are combined to carry out feedback linearization to system, improves the angular motion control precision of unmanned plane, and utilizes incremental local weighting method, and local model is automatically optimized, and the covering of new learning result to original result is avoided.