连续时间广义预测控制 CGPC
试验结果表明，加入聚类分析的径向基神经网络模型提高了连续预测的趋势准确率，降低了时间代价，并减小了模型的复杂度。
The result of this experiment shows that the modified RBF neuro-network increases trend accuracy in sequential predicting, while debasing the cost of time and reducing the complexity of the model.
针对多变量连续时间广义预测控制提出了一种新的时滞解决方案及其参数递推计算方法(MDCGPC)。
A new delay predictive solution and the parameters recursive computation method are proposed for the multivariable continuous-time generalized predictive control (MDCGPC).
同时，提出了连续性泄漏油品的时间离散方法，为冰区溢油行为的科学预测开辟了新的思路。
Meanwhile, it presents the time scattering method of successional leaking oil which inaugurates a new idea for the scientific prediction of oil spill behaviors.
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