In succession , this paper focuses on the design of inverse model control system using svm , and compares three kinds of inverse model control based on inverse model , that is , direct inverse control , pid compensated inverse model control . the control capability of these two controllers are simulated which validate the performance of controllers . as fuzzy inference system ( fis ) has poor self - learning ability , for the improving the learning ability of fis , this paper presents a svm - fis self - learning controller . both grads descending algorithm and chaotic optimization for the training of svm - fis self - learning controller are presented 接着,论文研究了基于支持向量机逆模型的控制器设计,提出了支持向量机直接逆模型控制、 pid补偿的支持向量机逆模型控制,通过仿真研究验证了所研究的这二种逆模型控制器的性能和效果;考虑到支持向量机具有很强的学习能力,文章还研究了利用支持向量机去提高模糊推理系统的学习能力、优化能力,从而提出了一种支持向量机-模糊推理自学习控制器,并对比研究了梯度法、混沌优化算法这二种学习算法,并给出了相应的仿真结果。
Firstly , the parameters controlling ductile crack growth are gotten from the resistance curve result of standard 3 point bending specimen , then the resistance curves of the tension specimen for the welded joint with different strength mis - matching are predicted based on the cell model 首先由标准三点弯曲试件的阻力曲线，得出反映材料微观损伤的特殊单元模型控制参量，再根据该参量对不同接头强度匹配下双边缺口拉伸试件的阻力曲线进行了定量预测，其结果与试验结果相当吻合。
Further , in order to overcome the disadvantage of the current fuzzy - model - based control design , this paper presents a fuzzy adaptive control scheme for complex nonlinear systems with uncertain nonlinearties and multiple time delays by synthesizing fuzzy t - s model and adaptive time - delay fuzzy logic systems 进一步,为了克服现有的基于模糊t - s模型控制方案的不足,文中针对具有不确定性和多重时延的复杂非线性系统,综合了模糊t - s模型和自适应时延模糊逻辑系统,提出了一种模糊自适应控制方法。
