ID 原文 译文
1943 随机生成 4、8、16 和 32 位数分别进行加减仿真操作，验证了全加器的正确性。 The simulation operation with random numberof 4, 8, 16 and 32 digits verifies the correctness of the full adder separately.
1944 该全加器量子代价较低，结构简单，有利于提高集成电路规模和集成度。 The low quantum cost and simple circuit struc-ture of the quantum full adder is helpful to improve the size and integration of integrated circuits.
1945 提出了一种基于二阶 Volterra 级数的语音信号非线性预测模型。 A type of nonlinear prediction model for speech signals based on second-order Volterra series is put for-ward.
1946 为克服传统的最小均方(Least MeanSquare，LMS)算法在模型核系数更新时的固有缺点，引入耗散均匀搜索粒子群优化算法(Dissipative Uniform ParticleSwarm Optimization，DUPSO)求解核系数， In order to overcome some intrinsic shortcomings caused by using the classic least mean square (LMS)algorithm toupdate Volterra model kernel coefficients, a dissipative uniform particle swarm optimization (DUPSO)algorithm is applied to obtain the kernel coefficients and then a DUPSO-SOVF prediction model can be constructed.
1947 并构建了 DUPSO-SOVF 预测模型;为避免传统方法中相空间的重构过程，构建了隐相空间 DUPSO-SOVF 预测模型，在求解模型核系数时动态地求解出最优嵌入维数和延迟时间; A DUPSO-SOVF predictionmodel with hidden phase space is constructed by dynamically obtaining parameters of embedding dimension and time delayin the process of solving model kernel coefficients rather than using traditional phase space reconstruction process.
1948 为降低模型复杂度，在误差允许范围内进行模型关键项的提取，从而减少了核系数个数，构建了少参数的 DUPSO-RPSOVF(ReducedParameter SOVF，RPSOVF)预测模型。 On the purpose to reduce model complexity, the key model kernels are extracted within the margin of the allowable error and themodel kernels are then reduced, and the reduced parameter DUPSO-SOVF (RPSOVF)prediction model is proposed.
1949 将英语音素、单词和短语作为实验样本数据进行仿真，结果表明:隐相空间 DUP-SO-SOVF 模型能够准确的计算出相空间重构参数， Simu-lation results for samples of English phonemes, words and phrases show that, the DUPSO-SOVF model with hidden phase space can accurately calculate parameters of embedding dimension and delay time of phase space reconstruction;
1950 DUPSO-SOVF 和 DUPSO-RPSOVF 两种预测模型对单帧和多帧语音信号均具有较高的预测精度，优于 PSO-SOVF 和 LMS-SOVF 预测模型， both of theDUPSO-SOVF model and the DUPSO-RPSOVF model exhibit higher prediction accuracy on single frame and multi-framespeech signal than PSO-SOVF and LMS-SOVF models.
1951 并且能够很好地反映语音序列变化的趋势和规律，可以满足语音序列预测的要求。 Also, the two proposed models can better reflect trends and regulari-ties of the speech signal series and meet requirements for speech signal prediction.
1952 异质网络相似度学习，即分析两个不同类型对象间的相关程度。 Heterogeneous network similarity learning is to analyze the degree of correlation between two differenttypes of objects.