期刊文献+
共找到362篇文章
< 1 2 19 >
每页显示 20 50 100
Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network 被引量:1
1
作者 杨建国 徐兰 +1 位作者 项前 刘彬 《Journal of Donghua University(English Edition)》 EI CAS 2014年第6期817-823,共7页
In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result... In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model. 展开更多
关键词 yarn quality prediction rough set(RS) knowledge discovery knowledge-based artificial neural network(KBANN)
下载PDF
Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources
2
作者 Hu Yinlei and Zhang YumingInstitute of Geology,SSB,Beijing 100029,China 《Earthquake Research in China》 1997年第2期64-72,共9页
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl... In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized. 展开更多
关键词 artificial neural network Method based on Expert knowledge and Its Application to Quantitative Identification of Potential Seismic Sources LENGTH
下载PDF
Establishing the knowledge repository of rapidly solidified aging Cu-Cr-Zr alloy on the artificial neural-network 被引量:3
3
作者 SUJuanhua DONGQiming +3 位作者 LIUPing LIHejun KANGBuxi TIANBaohong 《Rare Metals》 SCIE EI CAS CSCD 2004年第2期171-175,共5页
The non-linear relationship between parameters of rapidly solidified agingprocesses and mechancal and electrical properties of Cu-Cr-Zr alloy is available by using asupervised artificial neural network (ANN). A knowle... The non-linear relationship between parameters of rapidly solidified agingprocesses and mechancal and electrical properties of Cu-Cr-Zr alloy is available by using asupervised artificial neural network (ANN). A knowledge repository of rapidly solidified agingprocesses is established via sufficient data learning by the network. The predicted values of theneural network are in accordance with the tested data. So an effective measure for foreseeing andcontrolling the properties of the processing is provided. 展开更多
关键词 Cu-Cr-Zr alloy knowledge repository artificial neural network rapidsolidifiation aging
下载PDF
FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK 被引量:2
4
作者 李如强 陈进 伍星 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2006年第1期99-108,共10页
A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from ... A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks. 展开更多
关键词 rotating machinery fault diagnosis rough sets theory fuzzy sets theory generic algorithm knowledge-based fuzzy neural network
下载PDF
Artificial Neural Networks for Event Based Rainfall-Runoff Modeling
5
作者 Archana Sarkar Rakesh Kumar 《Journal of Water Resource and Protection》 2012年第10期891-897,共7页
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model... The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input 展开更多
关键词 artificial neural networks (ANNs) EVENT based RAINFALL-RUNOFF Process Error BACK Propagation neural Power
下载PDF
Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method 被引量:1
6
作者 Xueye WANG and Huang SONG (Department of Chemistry, Xiangtan University, Xiangtan 411105, China) Guanzhou QIU and Dianzuo WANG (Department of Mineral Engineering, Central South University of Technology, Changsha 410083, China) 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2000年第4期435-438,共4页
Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distribu... Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides. 展开更多
关键词 Prediction of Superconductivity for Oxides based on Structural Parameters and artificial neural network Method
下载PDF
Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network
7
作者 WANG Zhi-liang,FU Qiang,LIANG Chuan (Hydroelectric College,Sichuan University) 《Journal of Northeast Agricultural University(English Edition)》 CAS 2001年第1期37-42,共6页
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal... On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible. 展开更多
关键词 SOIL Prediction Model of Soil Nutrients Loss based on artificial neural network
下载PDF
Lateral interaction by Laplacian‐based graph smoothing for deep neural networks
8
作者 Jianhui Chen Zuoren Wang Cheng‐Lin Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1590-1607,共18页
Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modalit... Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models. 展开更多
关键词 artificial neural networks biologically plausible Laplacian‐based graph smoothing lateral interaction machine learning
下载PDF
Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection
9
作者 Ouyang Liu Kun Li +2 位作者 Ziwei Yin Deyun Gao Huachun Zhou 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2955-2977,共23页
Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks... Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases. 展开更多
关键词 DDoS attack knowledge graph multi-domain knowledge base graph neural network federated learning
下载PDF
Prediction of flow stress of Ti-15-3 alloy with artificial neural network 被引量:2
10
作者 李萍 单德彬 +2 位作者 薛克敏 吕炎 许沂 《中国有色金属学会会刊:英文版》 CSCD 2001年第1期95-97,共3页
Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and tem... Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [ 展开更多
关键词 artificial neural network Ti-15-3 ALLOY flow STRESS
下载PDF
Artificial Intelligence Embedded Object-Oriented Methodology For Model Based Decision Support 被引量:1
11
作者 Feng Shan Tian Yuan Li Tong & Cai Jun (Institute of System Engineering, Department of Automatic Control Engineering Huazhong University of Science and Technology, Wuhan, Hubei, 430074, P. R. China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1996年第1期1-14,共14页
The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strate... The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section. 展开更多
关键词 artificial intelligence Object-oriented methodology knowledge-based systems Intelligently cooperative systems neural nets Case hased reasoning Behavioral science Advancedautomation.
下载PDF
Knowledge-Based Systems for the Assessment and Management of Bridge Structures: A Review
12
作者 Ayaho Miyamoto 《Journal of Software Engineering and Applications》 2021年第10期505-536,共32页
It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a meth... It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items<span style="font-family:;" "=""> </span><span style="font-family:;" "="">which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms.</span><span style="font-family:;" "=""> </span><span style="font-family:;" "="">Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (<i>J-BMS BREX</i>) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.</span> 展开更多
关键词 knowledge-based System INFRASTRUCTURE BRIDGE Maintenance MANAGEMENT Expert System Reliability neural network Fuzzy Reasoning Genetic Algorithm
下载PDF
Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA 被引量:1
13
作者 Zheng Xian-zhe Gao Feng +2 位作者 Fu Ke-sen Lu Tian-lin Zhu Chong-hao 《Journal of Northeast Agricultural University(English Edition)》 CAS 2023年第1期69-84,共16页
To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexe... To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits. 展开更多
关键词 raspberry puree continuous microwave drying response surface method(RSM) artificial neural network(ANN) genetic algorithm(GA)CLC number:TG376 Document code:A Article ID:1006-8104(2023)-01-0069-16
下载PDF
Artificial Neural Networks in Risk Management: A Bibliometric Study
14
作者 Breno Gontijo Tavares Carlos Eduardo Sanches da Silva Adler Diniz de Souza 《Journal of Business Administration Research》 2021年第1期29-36,共8页
This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management.The study considered articles from the I.S.I.Web of Knowledge and Scopus databases,Identification of publishers,countries,pe... This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management.The study considered articles from the I.S.I.Web of Knowledge and Scopus databases,Identification of publishers,countries,periodicals and the keywords most frequently cited.We used the CiteSpace® software to analyze this material,which provides a set of features to support bibliometrics,including the reference maps.This study provides data collection on Artificial Neural Networks applied to risk management.The number of works identified in this study is significant,and in the last ten years,the number of citations has increased.We did not identify the increase in paper count within the same period. 展开更多
关键词 Bibliometric study artificial neural networks Risk management I.S.I.Web of knowledge SCOPUS
下载PDF
An elasto-plastic constitutive model of moderate sandy clay based on BC-RBFNN 被引量:1
15
作者 彭相华 王智超 +2 位作者 罗涛 余敏 罗迎社 《Journal of Central South University》 SCIE EI CAS 2008年第S1期47-50,共4页
Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in diffe... Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in different fields.In allusion to this,an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties.Firstly,knowledge base was established on triaxial compression testing data;then the model was trained,learned and emulated using knowledge base;finally,predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model.The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision,which provides possibility for engineering practice on demanding high precision. 展开更多
关键词 ELASTO-PLASTIC CONSTITUTIVE model artificial neural network BC-RBFNN(based on clustering radial basis function neural network) MODERATE SANDY clay
下载PDF
Physics-data coupling-driven method to predict the penetration depth into concrete targets
16
作者 Shuai Qin Hao Liu +2 位作者 Jianhui Wang Qiang Zhao Lei Zhang 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第3期184-192,共9页
The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.Ho... The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets. 展开更多
关键词 Penetration into concrete artificial neural networks Prior knowledge fusion Prediction model
下载PDF
HYBRID STRATIFIED ATMS AND ANN FOR CASE-BASED REASONING
17
作者 杨杰 黄欣 陆正刚 《Journal of Shanghai Jiaotong university(Science)》 EI 1999年第1期18-23,共6页
Case Based Reasoning (CBR) is a powerful problem solving technique in AI, but the traditional CBR techniques have its limitations. We hybridized stratified ATMS and ANN for CBR which can deal with case representation... Case Based Reasoning (CBR) is a powerful problem solving technique in AI, but the traditional CBR techniques have its limitations. We hybridized stratified ATMS and ANN for CBR which can deal with case representation, case retrieving, case adapting, learning from failure more effectively. The structure of our CBR system and algorithms of case base reasoning in our CBR system were presented. 展开更多
关键词 case based REASONING (CBR) STRATIFIED assumption based TRUTH maintenance system (ATMS) artificial neural network (ANN)
下载PDF
Autonomous Kernel Based Models for Short-Term Load Forecasting
18
作者 Vitor Hugo Ferreira Alexandre Pinto Alves da Silva 《Journal of Energy and Power Engineering》 2012年第12期1984-1993,共10页
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown adv... The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem. 展开更多
关键词 Load forecasting artificial neural networks input selection kernel based models support vector machine relevancevector machine.
下载PDF
Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks 被引量:4
19
作者 Jun Yin Wayne Xin Zhao Xiao-Ming Li 《Journal of Computer Science & Technology》 SCIE EI CSCD 2017年第4期805-813,共9页
Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural langua... Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system. 展开更多
关键词 question answering deep neural network knowledge base
原文传递
Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks 被引量:13
20
作者 TIAN ZHANG JIA WANG +5 位作者 QI LIU JINZAN ZHOU JIAN DAI XU HAN YUE ZHOU KUN XU 《Photonics Research》 SCIE EI CSCD 2019年第3期368-380,共13页
In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure(PWCCS) based on ar... In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure(PWCCS) based on artificial neural networks(ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs.More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis,defect detection, theoretical modeling, optical interconnects, and so on. 展开更多
关键词 EFFICIENT spectrum prediction artificial neural networks PLASMONIC WAVEGUIDE systems based
原文传递
上一页 1 2 19 下一页 到第
使用帮助 返回顶部