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学者姓名：严爱军
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Abstract ：
This paper proposes an optimization method based on the Black Hole Cuckoo Search Algorithm (BH-CS) to improve the accuracy of the weighted similarity measure in the case-based reasoning (CBR) model. First, take the root mean square error of the case-based reasoning prediction model as the fitness function. Secondly, use the Levy flight of the Cuckoo Search algorithm to update the feature weights in the weighted similarity measure method and evaluate the optimal weights from them. Then, randomly generate new feature weights with some probability. Finally, use the Black Hole algorithm to optimize feature weights further to obtain optimal weights and optimize the case similarity measure. The optimization method was tested using UCI standard data set. The results show that the BH-CS algorithm has an advantage over other algorithms in improving the accuracy of case similarity measures and can effectively improve the prediction accuracy of the CBR model.
Keyword ：
Weighted Similarity Measure Weighted Similarity Measure Cuckoo Search Algorithm Cuckoo Search Algorithm Black Hole Algorithm Black Hole Algorithm Feature Weight Feature Weight
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| GB/T 7714 | Yan, Aijun , Li, Jiaxuan . Case Weighted Similarity Measure Optimization Method Based On Black Hole Cuckoo Search Algorithm [J]. | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) , 2021 : 6269-6274 . |
| MLA | Yan, Aijun 等. "Case Weighted Similarity Measure Optimization Method Based On Black Hole Cuckoo Search Algorithm" . | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) (2021) : 6269-6274 . |
| APA | Yan, Aijun , Li, Jiaxuan . Case Weighted Similarity Measure Optimization Method Based On Black Hole Cuckoo Search Algorithm . | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) , 2021 , 6269-6274 . |
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Abstract ：
Feature weights have a significant effect on the accuracy of case-based reasoning (CBR) prediction models. Therefore, this paper proposes a feature weight optimization method combining a t-distribution mutation operator and a memetic algorithm (t-memetic). In this method, the mean absolute percentage error of the CBR prediction model is defined as a fitness function, and the memetic framework is used to realize the optimization process for case weights. A sparrow search algorithm and adaptive t-distribution mutation operator are used to realize the global search of the case feature weights, and the simulated annealing algorithm is used to perform a local search of the weighted individuals with the best fitness for the current population. Five UCI standard regression datasets are used respectively to test the effectiveness of the proposed method and compare it with classical feature weight optimization algorithms. The results show that the CBR prediction model has the highest accuracy after the weights are optimized by the t-memetic algorithm, which indicates that the proposed weight optimization method can be used effectively in CBR prediction models.
Keyword ：
Case-based reasoning Case-based reasoning t-distribution mutation operator t-distribution mutation operator Feature weights Feature weights Memetic algorithm Memetic algorithm Sparrow search Sparrow search Simulated annealing Simulated annealing
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| GB/T 7714 | Yan, Aijun , Guo, Yidong . Feature Weight Optimization Method Based on t-Memetic Algorithm [J]. | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) , 2021 : 6275-6280 . |
| MLA | Yan, Aijun 等. "Feature Weight Optimization Method Based on t-Memetic Algorithm" . | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) (2021) : 6275-6280 . |
| APA | Yan, Aijun , Guo, Yidong . Feature Weight Optimization Method Based on t-Memetic Algorithm . | 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) , 2021 , 6275-6280 . |
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Abstract ：
案例推理分类器的权重分配及案例库维护方法
Keyword ：
案例库维护 案例库维护 案例推理 案例推理 蚁狮算法 蚁狮算法 权重分配 权重分配 分类器 分类器
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| GB/T 7714 | 严爱军 , 魏志远 , 计算机应用 . 案例推理分类器的权重分配及案例库维护方法 [J]. | 严爱军 , 2021 , 41 (4) : 1071-1077 . |
| MLA | 严爱军 等. "案例推理分类器的权重分配及案例库维护方法" . | 严爱军 41 . 4 (2021) : 1071-1077 . |
| APA | 严爱军 , 魏志远 , 计算机应用 . 案例推理分类器的权重分配及案例库维护方法 . | 严爱军 , 2021 , 41 (4) , 1071-1077 . |
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Abstract ：
In order to alleviate the negative impact of noise on the accuracy of deep stochastic configuration network modeling, a robust deep stochastic configuration network modeling method based on kernel density estimation is proposed. The output weights of each hidden layer of deep stochastic configuration networks are obtained by solving the weighted least square problem, where the kernel density estimation method is employed to set the penalty weights of training samples. In addition, the alternating optimization technique is applied to update the penalty weights and hidden layer output weights. The effectiveness of the proposed method is tested and evaluated by using function approximation and the benchmark dataset. The results show that the proposed method can effectively alleviate the impact of noise on modeling accuracy, which is valuable for applications in the field of robust modeling.
Keyword ：
Alternating Optimization Techniques Alternating Optimization Techniques Kernel Density Estimation Kernel Density Estimation Deep Stochastic Configuration Network Deep Stochastic Configuration Network Robust Modeling Robust Modeling
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| GB/T 7714 | Guo, Jingcheng , Yan, Aijun . Robust Deep Stochastic Configuration Network Modeling Method Based on Kernel Density Estimation [J]. | PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) , 2021 : 575-579 . |
| MLA | Guo, Jingcheng 等. "Robust Deep Stochastic Configuration Network Modeling Method Based on Kernel Density Estimation" . | PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) (2021) : 575-579 . |
| APA | Guo, Jingcheng , Yan, Aijun . Robust Deep Stochastic Configuration Network Modeling Method Based on Kernel Density Estimation . | PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) , 2021 , 575-579 . |
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Abstract ：
为了解决基于数据的预测模型中特征权重分配不合理的问题,将鲸鱼优化算法(whale optimization algorithm, WOA)纳入文化算法的种群空间中,获得了一种文化鲸鱼优化算法(cultural whale optimization algorithm, CWOA)以用于特征权重的优化分配.首先,将预测模型的均方根误差作为适应度函数;然后,采用WOA在种群空间中对特征权重进行迭代寻优;接着,通过接受函数将种群空间中的最优权重置于信仰空间中进行性能评价与双变异演化,以此形成形势知识和规范知识;最后,通过影响函数对种群空间中的权重进行更新指导,如此循环,从而得到特征权重的优化分配结果...
Keyword ：
信仰空间 信仰空间 鲸鱼优化算法 鲸鱼优化算法 权重分配 权重分配 文化算法 文化算法 种群空间 种群空间 案例推理 案例推理
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| GB/T 7714 | 严爱军 , 曹付起 . 基于文化鲸鱼优化算法的特征权重优化分配方法 [J]. | 北京工业大学学报 , 2021 , 47 (11) : 1230-1238 . |
| MLA | 严爱军 等. "基于文化鲸鱼优化算法的特征权重优化分配方法" . | 北京工业大学学报 47 . 11 (2021) : 1230-1238 . |
| APA | 严爱军 , 曹付起 . 基于文化鲸鱼优化算法的特征权重优化分配方法 . | 北京工业大学学报 , 2021 , 47 (11) , 1230-1238 . |
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Abstract ：
在垃圾焚烧的过程中,垃圾热值的波动会影响垃圾焚烧的稳定性.为了实现城市生活垃圾热值的实时在线预测以及变化趋势预测,采用模糊神经网络软测量方法,利用焚烧发电厂在线运行数据作为输入,实现垃圾热值的实时预测功能.首先采用互信息方法从若干特征变量中剔除部分无关变量;然后将模糊神经网络和粒子群优化算法结合起来从上述选择出的特征变量中进一步剔除冗余变量,从而确定预测垃圾热值的输入变量,并从中训练出垃圾热值的模糊神经网络预测模型;最后通过采集的样本数据进行性能测试.结果表明该方法有较好的预测准确率和实时性,适用于垃圾热值的在线预测.
Keyword ：
热值 热值 城市生活垃圾 城市生活垃圾 实时预测 实时预测 模糊神经网络 模糊神经网络 特征选择 特征选择 互信息 互信息
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| GB/T 7714 | 丁晨曦 , 严爱军 . 城市生活垃圾热值的特征变量选择方法及预测建模 [J]. | 北京工业大学学报 , 2021 , 47 (08) : 874-885 . |
| MLA | 丁晨曦 等. "城市生活垃圾热值的特征变量选择方法及预测建模" . | 北京工业大学学报 47 . 08 (2021) : 874-885 . |
| APA | 丁晨曦 , 严爱军 . 城市生活垃圾热值的特征变量选择方法及预测建模 . | 北京工业大学学报 , 2021 , 47 (08) , 874-885 . |
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Abstract ：
为了城市生活垃圾焚烧过程建模、控制、优化等方法的研究测试,将实物控制系统与虚拟对象结合起来研发了一种具有三层结构的半实物仿真平台.该平台的实物控制系统由智能控制优化层和基础控制层组成;虚拟对象层包括软件模拟的仪表、执行机构装置和焚烧过程模型.开发了人机界面、设备与参数的监控、焚烧过程模型以及OPC通讯等软件.测试了智能控制优化层和基础控制层的各项功能,结果表明:该平台的软、硬件部分运行稳定而可靠,能够有效而正确反映焚烧过程的变化.
Keyword ：
城市生活垃圾 城市生活垃圾 半实物仿真 半实物仿真 焚烧过程 焚烧过程 监控 监控
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| GB/T 7714 | 严爱军 , 夏恒 , 刘溪芷 . 城市生活垃圾焚烧过程监控半实物仿真平台研发 [J]. | 系统仿真学报 , 2021 , 33 (6) : 1427-1435 . |
| MLA | 严爱军 等. "城市生活垃圾焚烧过程监控半实物仿真平台研发" . | 系统仿真学报 33 . 6 (2021) : 1427-1435 . |
| APA | 严爱军 , 夏恒 , 刘溪芷 . 城市生活垃圾焚烧过程监控半实物仿真平台研发 . | 系统仿真学报 , 2021 , 33 (6) , 1427-1435 . |
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Abstract ：
It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic algorithms (GAs) and stochastic configuration networks (SCNs), and the SCN-based incinerator temperature model is obtained simultaneously. Firstly, filter feature selection is realized by calculating the MI value between each feature variable and the incinerator temperature from historical data. Secondly, the fitness function of GAs is defined by the root mean square error of the incinerator temperature obtained by training SCNs, and features obtained by MI methods are searched iteratively to complete the wrapper feature selection, where the SCN-based incinerator temperature prediction model is obtained. Finally, the proposed model is verified by MSW incinerator temperature historical data. The results show that the SCN-based prediction model using the hybrid selection method can better predict the change trend of incinerator temperature, which proves that the SCNs has great development potential in the field of prediction modeling.
Keyword ：
municipal solid waste municipal solid waste incinerator temperature prediction incinerator temperature prediction feature selection feature selection stochastic configuration networks stochastic configuration networks
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| GB/T 7714 | Guo, Jingcheng , Yan, Aijun . Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature [J]. | SENSORS , 2021 , 21 (23) . |
| MLA | Guo, Jingcheng 等. "Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature" . | SENSORS 21 . 23 (2021) . |
| APA | Guo, Jingcheng , Yan, Aijun . Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature . | SENSORS , 2021 , 21 (23) . |
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Abstract ：
由于特征权重分配以及案例库维护对案例推理(CBR)分类器的性能有重要影响,提出了用蚁狮(ALO)算法来分配权重且用高斯混合模型的期望最大化算法(GMMEM)进行案例库维护的案例推理算法模型——AGECBR(Ant Lion and Expectation Maximization of Gaussian Mixture Model Case-Based Reasoning).首先采用蚁狮算法对特征权重进行分配,在这个过程中将案例推理分类准确率作为蚁狮算法对特征权重进行迭代寻优的适应度函数,以此实现特征权重的优化分配;然后,使用高斯混合模型的期望最大化算法对案例库中的各案例进行聚类分析,并删除其中的噪声案例和冗余案例,从而实现案例库的维护.在UCI标准数据集上进行了实验,所提模型AGECBR比反向传播(BP)、k-近邻(kNN)等分类算法平均分类准确率提升了3.83～5.44个百分点.实验结果表明,AGECBR能够使案例推理分类准确率得到有效改进.
Keyword ：
案例库维护 案例库维护 权重分配 权重分配 蚁狮算法 蚁狮算法 分类器 分类器 案例推理 案例推理
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| GB/T 7714 | 严爱军 , 魏志远 . 案例推理分类器的权重分配及案例库维护方法 [J]. | 计算机应用 , 2021 , 41 (4) : 1071-1077 . |
| MLA | 严爱军 等. "案例推理分类器的权重分配及案例库维护方法" . | 计算机应用 41 . 4 (2021) : 1071-1077 . |
| APA | 严爱军 , 魏志远 . 案例推理分类器的权重分配及案例库维护方法 . | 计算机应用 , 2021 , 41 (4) , 1071-1077 . |
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Abstract ：
Dioxin (DXN) is a highly toxic pollutant emitted during municipal solid waste incinerator (MSWI) process. In the actual industrial process, DXN emission concentration is measured through offline experiment analysis, which has shortcomings such as long time and high cost. In this study, a soft-sensing model of DXN emission concentration was established by using MSWI process variables. Random forest (RF) and gradient boosting decision tree algorithms are used to construct ensemble learning-based DXN model. First, RF tree sub-models are constructed base on random sampling and CART regression tree. Then, Gradient boosting decision tree (GBDT) method is used to each RF sub-model, in which one gradient iteration is performed to reduce the prediction error. Finally, a simple average combination strategy is performed on these RF and GBDT based sub-models. Thus, the soft measuring model of DXN emission concentration based on small samples and high-dimensional MSWI process data is obtained. The proposed method can both reduce model variance and eliminate prediction bias. The experimental results show that the proposed method can further improve the prediction performance and generalization ability.
Keyword ：
Dioxin (DXN) emission concentration Dioxin (DXN) emission concentration Soft measuring model Soft measuring model Municipal solid waste incinerator (MSWI) Municipal solid waste incinerator (MSWI) Gradient boosting decision tree (GBDT) Gradient boosting decision tree (GBDT) Random forest (RF) Random forest (RF)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Qiao, Junfei et al. Soft Measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT [C] . 2020 : 2173-2178 . |
| MLA | Xia, Heng et al. "Soft Measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT" . (2020) : 2173-2178 . |
| APA | Xia, Heng , Tang, Jian , Qiao, Junfei , Yan, Aijun , Guo, Zihao . Soft Measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT . (2020) : 2173-2178 . |
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