用于T细胞表位预测的分类器集成方法*

T细胞表位预测技术对于减少实验合成重叠肽,理解T细胞介导的免疫特异性和研制亚单位多肽及基因疫苗均有重要意义.为弥补已有基于机器学习方法的T细胞表位预测模型的可理解性的不足并进一步提高模型的预测精度,首先通过肽的预处理构建出了存储等长肽段的决策表,而后提出了基于粗糙集的分类器集成算法.该算法不但综合利用了基于信息熵的属性约简完备算法和其他属性约简算法的优势,而且将T细胞表位预测领域中的锚点知识融入到了属性值约简过程中.最后利用该算法来预测MHC Ⅱ类分子HLA-DR4(B1·0401)的结合肽,首次提取出了预测精度高且能帮助专家理解MHC分子与抗原肽的结合机理的产生式规则,为下一步的分子建模工作奠定了基础.     

基于粗集的T细胞表位预测方法

T细胞表位预测技术对于减少实验合成重叠肽、研究病原体与机体作用的免疫机制以及深入理解T细胞介导的免疫特异性均有重要意义。为增强T细胞表位预测模型的可理解性,本文在通过肽的预处理构建出存储等长肽段的决策表之后,设计出了一种基于粗集的T细胞表位预测方法。该方法由基于信息熵的属性约简完备算法和基于锚点知识的属性值顺序约简改进算法共同组成。基于HLA-DR4（B10401）编码的MHCII类分子结合肽的实验数据表明,在预测精度与传统神经网络方法大致相当的基础上,本文方法可以提取出用于帮助专家理解MHC分子与抗原肽结合机理的产生式规则。     

伴随时空特性的雷电预测BP-ANN模型研究

为提高雷电预测模型的准确率和学习性能，提出一种基于增量学习和时空特性的雷电预测BP-ANN二项分类器。通过增量方式和依据数据的时空特征进行历史数据的学习，建立多种BP-ANN模型，分别对新的数据进行预测分类，然后采用多数投票方式确定新数据的类别。分别构建基于增量学习的BP-ANN模型、基于时空特性的BP-ANN模型以及结合基于增量学习和时空特性的BP-ANN模型这3种雷电预测模型，并在真实雷电数据集上进行预测准确度和学习性能的测试，结果表明了增量学习、时空特性以及二者结合的优劣。     

支持向量机与神经网络相结合的板带凸度预测

为提高热轧生产过程中板带凸度的预测精度,提出了一种将粒子群优化算法(particle swarm optimization, PSO)、支持向量回归(support vector regression, SVR)和BP神经网络(back propagation neural network, BPNN)相结合的板带凸度预测模型。采用PSO算法优化SVR模型的参数,建立了PSO-SVR板带凸度预测模型,提出采用BPNN建立板带凸度偏差模型与PSO-SVR板带凸度模型相结合的方法对板带凸度进行预测。采用现场数据对模型的预测精度进行验证,并采用统计指标评价模型的综合性能。仿真结果表明,与PSO-SVR、SVR、BPNN和GA-SVR模型进行比较,PSO-SVR+BPNN模型具有较高的学习能力和泛化能力,并且比GA-SVR模型运算时间短。     

基于IIGA-BP神经网络的钢材销售预测模型

为克服传统BP神经网络(BP Neural Network,BPNN)在销售预测中,预测精度低、收敛速度慢的缺点.提出了一种基于改进免疫遗传算法(Improved Immune Genetic Algorithm,IIGA)优化BP神经网络的销售预测模型.改进的免疫遗传算法提出了新的种群初始化方式、抗体浓度的调节机制及自适应交叉算子、变异算子的设计方法,有效的提高了IIGA的收敛能力和寻优能力.并用IIGA优化BPNN的初始权值和阈值,改善网络参数的随机性导致BPNN输出不稳定和易陷入局部极值的缺点.以某钢铁企业的历史销售数据为例进行实证研究,利用Matlab分别构建BP、IGA-BP和IIGA-BP神经网络预测模型进行仿真对比分析.实验证明,IIGA-BP神经网络预测模型较BP神经网络预测模型预测精度提高了23.82％,较IGA-BP神经网络预测模型预测精度提高了22.02％.IIGA-BP神经网络模型对钢材销售预测的泛化性能更好,预测效果更稳定误差基本保持在[0.25,0.25]之间,预测精度大幅度提高,为企业销售预测提供了一种较为有效的方法.     

基于PCA-BPNN方法的中长期电力负荷预测

针对基于反向传播神经网络(Back-Propagation Neural Network,BPNN)的中长期电力负荷预测算法中,预测模型的精度和泛化能力易受输入样本变量影响这一问题,利用主元分析(Principal Component Analysis,PCA)方法能消除变量间相关性的特,对BPNN的输入空间进行重构,消除重叠信息,提取主导因素,优化了网络结构,提高了预测精度.通过实例验证了该方法的有效性.此方法可以使用电计划部门实时、准确的预测电力负荷,以此最优的配比发电机组,也可减少由于预测不准确带来的电力系统各种故障的发生.     

基于FCGA和改进LSTM-BPNN的燃气负荷预测

准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型.首先第一阶段先用LSTM进行燃气负荷初步预测,然后计算出燃气负荷残差值,第二阶段先用BPNN去预测残差值,然后用Adam自适应学习率算法在学习过程中自动调节LSTM-BPNN残差模型的学习率,加快拟合速度,接着用模糊编码遗传算法去优化BPNN的初始权重和阈值,以便寻找到全局最优解.最后把两阶段的预测值和作为最终的燃气负荷预测值.通过对比实验得出,本文模型比单模型,原始两阶段预测模型得到了更高的预测准确率.     

基于蚁群算法优化反向传播神经网络的软件质量预测

针对基于反向传播神经网络（BPNN）的软件质量预测模型存在收敛慢、模型精度不高的问题,提出一种基于蚁群算法优化BPNN的软件质量预测（SQP-ACO-BPNN）方法。首先,选择软件质量评价指标,确立软件质量评价体系;其次,采用BPNN构建初始软件质量预测模型,并利用蚁群优化（ACO）算法确定若干网络结构、网络初始连接权值和阈值;再次,给出网络结构评价函数,选择神经网络模型的最佳结构、网络初始连接权值和阈值;最后,通过BP算法训练该网络,得到最终的软件质量预测模型。在机载嵌入式软件质量预测数据上的实验结果表明,优化后的BPNN模型有效提高了预测的准确率、精确率、召回率和F1值,并且模型能够更快收敛,验证了SQP-ACO-BPNN方法的有效性。     

基于小波阈值和BPNN的冷轧煤气消耗预测

为实现钢铁企业冷轧煤气消耗量的精准预测，提出了一种基于小波阈值和BP神经网络（BPNN）的预测模型。利用小波阈值对煤气消耗数据进行去噪预处理，筛选出合理数据，采用BP神经网络预测煤气消耗量。实验结果表明，与其他方法相比，小波阈值和BPNN模型的预测精度更高，为钢铁企业煤气合理调度、减少排放及提高能源利用率提供有力支撑。     

基于 PCA-BP 神经网络的股票价格预测研究

在股票决策问题的研究中,针对影响股票价格因素间存在高度的非线性、存在数据冗余等特征,传统股票预测方法无法消除数据之间冗余和捕捉非线性规律导致预测精度较低,为了提高股票价格预测精度,提出一个基于主成份分析(PCA)的 BP 神经网络(BPNN)股票预测模型(PCA-BPNN).首先对影响股票价格波动的各因素进行主成份分析,消除各因素之间的冗余性,降低 BP 神经网络的输入维数,加快 BP 神经网络测速度并提高预测精度,然后利用 BPNN 对保留成分进行建模预测.利用 PEA-BPNN 模型对上海证券交易所上市的首创股份(600008)经济数据进行了验证性测试和分析,结果表明,PEA-BPNN 模型预测精度显著提高,是一种高效和准确的股票预测模型.     

Estimation of formation strength index of aquifer from neural networks

The purpose of this study is to construct a model that predicts an aquifer's formation strength index (the ratio of shear modulus and bulk compressibility, G/C_b) from geophysical well logs by using a back-propagation neural network (BPNN). The BPNN model of an aquifer's formation strength index is developed using a set of well logging data. The model is a [4-5-1] three-layer BPNN with a four-neuron input layer (depth, gamma-ray log data, formation density log data, and sonic log data, respectively), a five-neuron hidden layer, and a one-neuron output layer (formation strength index).The optimal learning rate and momentum constant used in the BPNN model are obtained from serial combinative experiments. The inside test and outside test are implemented to check the performance of network learning and the prediction ability of the network, respectively. The results of the inside test, based on 84 training data sets from a total of 105 data sets, show that the network has been well-trained because the mean square error between the network output value and the target value from the inside test is very small (1.1×10⁻⁴). The results of the outside test, based on 21 testing data sets from 105 data sets, show the excellent prediction ability of the BPNN model, because the network prediction values closely track with the target values (the mean square error is 2.1×10⁻⁴).     

基于改进教学算法优化BP 神经网络的催化剂碳含量预测模型

为提高待生催化剂碳含量预测的准确性, 提出一种基于改进的教学算法(MTLBO) 来优化BP 神经网络的预测模型. 针对基础教学算法全局搜索能力差的问题, 在教师阶段前后增加了预习和复习过程, 并在学生阶段采用量子方式进行更新. 测试结果表明, 该改进能够提高教学算法全局探索和局部改良能力, 利用改进教学算法可优化BP神经网络的权值和阈值, 并进行待生催化剂碳含量预测. 仿真结果表明, 改进后预测模型的预测精度和泛化能力均有一定程度的提高.

     

基于HI-DD-AdaBoost.RT的锂离子动力电池SOH预测

锂离子电池是一个复杂的电化学动态系统,实时准确的健康状态(SOH)估计对电动汽车动力锂电池的维护至关重要,传统建模方法难以实现SOH的在线估算.基于此,从实时评估电池的SOH出发,在增量学习的基础上,选取与电池健康状态相关的指标建立SOH预测模型.考虑到增量学习中的耗时性问题,提出融合滑动窗口技术的HI-DD算法,该算法可以检测概念漂移是否发生,从而指导和确定模型更新位置;设计出HI-DD与AdaBoost.RT结合的模型更新策略,进而提高模型的在线学习性能和预测精度,最后使用CALCE提供的电池老化实验数据对所提出的方法进行验证.结果表明,基于增量学习的HI-DD-AdaBoost.RT预测算法具有较强的在线更新能力和较高的预测精度,能够满足SOH在线预测的实际需求.     

基于支持向量机的丙酮精制建模

采用支持向量机建立了丙酮精制过程的产品质量与生产工艺参数之间的预测模型,并将其与反向传播神经网络和径向基神经网络模型相比较。在实际工业数据上进行的实验结果表明,支持向量机模型对丙酮纯度具有良好的预测效果,性能优于反向传播神经网络和径向基网络模型。     

Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

In this paper, two connectionist models are proposed based on different learning paradigms, viz., back propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) to predict the first lactation 305-day milk yield (FLMY305) in Karan Fries (KF) dairy cattle. Also, a conventional multiple linear regression (MLR) model is developed for the prediction. In this study, all the models have been developed using a scientifically determined optimum dataset of representative breeding traits of the cattle. The prediction performances of the connectionist models are compared with that of the conventional model. This study shows that the RBFNN model performs relatively better than the MLR model. However, the BPNN model performs more or less in the close vicinity of the conventional MLR model. Hence, it is inferred that the connectionist models have potential as an alternative to the conventional models for predicting FLMY305 in KF cattle.     

A dynamic meta-learning rate-based model for gold market forecasting

In this paper, an improved EMD meta-learning rate-based model for gold price forecasting is proposed. First, we adopt the EMD method to divide the time series data into different subsets. Second, a back-propagation neural network model (BPNN) is used to function as the prediction model in our system. We update the online learning rate of BPNN instantly as well as the weight matrix. Finally, a rating method is used to identify the most suitable BPNN model for further prediction. The experiment results show that our system has a good forecasting performance.     

Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time.