5 results about "Inverse system" patented technology

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 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.

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.