2results about How to "Reduce repetitions" patented technology

The invention discloses an adaptive path training system and method based on capability evolution prediction, and the system comprises a data collection module which is used for collecting the real-time behavior data of a target training object in a training process; the capability state construction module is used for constructing capability state data according to the real-time behavior data; the capability trajectory prediction module is used for performing capability evolution prediction on the capability state data according to a preset time sequence prediction model to obtain a behavior evolution trajectory sequence; the training path generation module is used for constructing a target function on the basis of a preset training task set according to the behavior evolution trajectory sequence and in combination with training time and training cost, and solving the target function on the basis of a preset strengthening algorithm to obtain an optimal training path; and the training execution module is used for sequentially executing the corresponding training tasks based on the optimal training path. The technical problem that in the prior art, a training path is fixed, and a training object cannot be dynamically adjusted is solved.

This invention discloses a method for classifier parameter identification based on Extreme Learning Machine (ELM). Step 1: Divide the classification dataset into training and test datasets. Step 2: Construct an ELM model and use a modified whale optimization algorithm to obtain the optimal initialization parameters for the multi-novel ELM. Step 3: Train the ELM model online using the training dataset, evaluate the model using the structural risk loss function, identify and update the output weights, and complete the classification training of the obtained dataset to refine the ELM model parameters. Step 4: Input the test dataset to be classified into the multi-novel ELM model trained online in Step 3, and identify the category of the test dataset online. If new data is input, repeat Step 3 to classify the newly input classification dataset. This invention improves the classification accuracy of the ELM model.