CAJ | 학술논문

利用级联神经网络模型对多个三维目标进行识别，为提高其正确识别率，提出多种优化方法，它们可单独使用，也可以联合使用。对不变性编码、算法、互连权重的二值化方法、样本优选等进行了研究和探讨。利用优化后的模型对三个飞机模型在视场内的任意位置、任意取向(面内旋转360°，面外旋转大于45°)的投影进行识别。计算机模拟表明正确识别率达到96%以上。
이용급련신경망락모형대다개삼유목표진행식별，위제고기정학식별솔，제출다충우화방법，타문가단독사용，야가이연합사용。대불변성편마、산법、호련권중적이치화방법、양본우선등진행료연구화탐토。이용우화후적모형대삼개비궤모형재시장내적임의위치、임의취향(면내선전360°，면외선전대우45°)적투영진행식별。계산궤모의표명정학식별솔체도96%이상。
The cascaded neuron network model is used to recognize 3-Dtargets. In order to improve the recognizing rate, several methods of choosing the construct and algorithm are proposed. They can be used together or solely according to the requirement. The invariance encoding, the algorithm, the binarizing of the interconnection weights and the selecting methods of the training samples, and so on are studied. The computer simulation of recognizing three plane models is completed based on this cascaded neuron network model. The recognizing rate of the model is over 96% as the three planes arbitrarily positioned and directed.