农业机器人运动轨迹控制仿真分析——基于遗传算法优化和RBF网络逼近

随着国力的不断增长,我国科技产业发展突飞猛进,机械自动化、计算机控制系统和测试计量行业的不断发展,使得移动机器人的研究也达到了一个前所未有的高度,机器人已经被广泛地应用到农业生产、工业生产、国家安全、生活服务和高等研究设计等领域的各个方面。移动机器人作为机器人的一部分,集中了智能传感技术、机械制造、电子无线通信技术、智能仪器和自动化控制工程等多学科的研究成果,是当前科技研究与设计最前沿的领域之一。为此,基于遗传算法优化RBF网络逼近算法,根据机器人运动轨迹的特性,研究了机器人运动轨迹控制技术,通过实时传感器在线感知系统,为智能机器人实时规划出无碰撞、路线短的运动轨迹。实验结果表明:所研究的机器人运动轨迹优化技术,具有较好的控制作用,其在行进过程中能及时、有效避地开前方障碍,可靠性强,稳定性好,应用前景非常广阔。

Prediction of coal ash fusion temperature using constructive-pruning hybrid method for RBF networks

A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.