2results about How to "Robust method" patented technology

The application discloses a kind of based on selective activation's pulse neural network continuous learning target identification system, including computer memory, computer processor and the computer program stored in the computer memory and can be executed on the computer processor, the computer memory has the selective activation SNNs model of well-trained;Selective activation SNNs model includes the feature extraction part of pulse neural network front end, and trajectory-based K-WTA mechanism and variable threshold mechanism are used in rear end to carry out continuous learning;The computer processor executes the computer program and realizes the following steps: the image to be identified is input into well-trained selective activation SNNs model, and the identification classification result is obtained.The present application reduces the catastrophic forgetting by enhancing the neural dynamic characteristics in intelligent learning network (SNN), improves the target recognition effect of pulse neural network in continuous learning scene, and expands the application scene of pulse neural network.

The invention describes a device for evaluating sensor measurements (1.1), having: - a sensor (1), wherein for evaluating the sensor measurements (1.1) of the sensor (1) a model function suitable for least squares regression is provided, which can be defined by a parameter vector, wherein at least one parameter of the parameter vector forms a sensor output signal (3); and - a calculation and evaluation unit (2), having a neural network (2.1) for estimating the parameter vector on the basis of actually determined sensor measurements (1.1) and a least squares regression module (2.2), wherein the neural network (2.1) is trained with parameter vectors and associated sensor measurements, and the calculation and evaluation unit is set up to: ° determine at least one parameter estimate vector as an input variable for the least squares regression module (2.2) by means of the trained neural network (2.1) for sensor measurements (1.1) measured with the sensor (1), ° interrupt the least squares regression in the event that a convergence criterion is met when implementing the least squares regression, and ° output at least one parameter of the last determined parameter vector as a sensor output signal (3). An associated automated method for evaluating sensor measurements and the use of the device are also described.