A Forward Propagation Automatic Seeking Solution Artificial Intelligence Model

Most of the existing artificial intelligence models are based on the training of large data sets, but in some application fields, sample sets cannot be provided in advance. In order to solve this problem, a forward propagation automatic seeking solution artificial intelligence model is proposed. It isn’t necessary to prepare a large set of training samples in advance. The model coefficient is constantly optimized and adjusted by the feedback of the researched object, and the purpose of seeking solution of the researched object is finally achieved. Through a concrete experimental example, the proposed method is compared with the traditional artificial intelligence method so that the advantages and effectiveness of the proposed method are proved. This method can be widely used in the adaptive learning and research of the unknown objects.