2results about How to "Rich input" patented technology

This invention discloses a multi-factor driven logical sequence generation method and apparatus, comprising: a feature input module, a multi-factor fusion module, a dynamic constraint module, a logical generation module, a reordering and evaluation module, and a feedback learning module. The method involves receiving text information from multiple sources, performing structured parsing and normalization to obtain a standardized set of text feature vectors; extracting multi-factors and constructing multi-factor constraints; generating a multi-factor driven logical sequence based on the set of multi-factor constraints; performing dynamic information updates and output constraint adjustments; generating candidate logical sequences; and designing multiple-dimensional indicators for re-evaluation. This invention significantly improves the accuracy, adaptability, and optimality of logical text sequences by addressing the problems of insufficient input information processing, lack of dynamic adaptability, and insufficient post-optimization mechanisms in the logical text sequence generation process.

The application provides a robot dynamic error compensation method based on a capsule neural network, adopts Newton-Euler method to establish a robot dynamics model, and performs linearization and dynamics parameter identification; uses joint motion data as input to obtain predicted torque for model training by using the linearized dynamics model after the dynamics parameter identification, and calculates error between the predicted torque and actual torque; regards normalized parameters as vectors in a vector space, calculates inner products of the vectors to form a Gram matrix and converts the Gram matrix into a two-dimensional image; learns error between the predicted torque and the actual torque by using a capsule neural network, and obtains a trained capsule neural network; obtains real-time prediction error by using the trained capsule neural network, and compensates real-time predicted torque obtained by the linearized dynamics model after the parameter identification by using the real-time prediction error. The application realizes high-precision and adaptive dynamic error compensation.