A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradie...A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradient-descent algorithm. The model shows a much better performance on anti-jamming and nonlinear classification, and simulation is carried out to compare this method with other nonlinear channel equalization methods. The results show the method has the least bit error rate （BER）.展开更多
A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new ...A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.展开更多
In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested...In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.展开更多
Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has bee...Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.展开更多
为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制...为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制冷系统的反向传播(back-propagation,BP)神经网络故障诊断模型和决策树故障诊断模型。研究结果表明:当冷凝器风量降低时,压缩机排气温度与排气压力上升,空调系统的制冷量和性能系数(coefficient of performance,COP)下降。通过对比2种不同诊断策略的仿真测试结果发现,采用BP神经网络进行车载空调制冷系统故障诊断的准确率可以达到92.5%。展开更多
文摘A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradient-descent algorithm. The model shows a much better performance on anti-jamming and nonlinear classification, and simulation is carried out to compare this method with other nonlinear channel equalization methods. The results show the method has the least bit error rate （BER）.
文摘A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.
文摘In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.
文摘Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.
文摘为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制冷系统的反向传播(back-propagation,BP)神经网络故障诊断模型和决策树故障诊断模型。研究结果表明:当冷凝器风量降低时,压缩机排气温度与排气压力上升,空调系统的制冷量和性能系数(coefficient of performance,COP)下降。通过对比2种不同诊断策略的仿真测试结果发现,采用BP神经网络进行车载空调制冷系统故障诊断的准确率可以达到92.5%。
