杨波
长期从事计算机网络、智能控制与信息处理方面的科研工作
个性化签名
- 姓名:杨波
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学术头衔:
博士生导师
- 职称:-
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学科领域:
计算机应用
- 研究兴趣:长期从事计算机网络、智能控制与信息处理方面的科研工作
杨波,1965年生,山东临沂人,中共党员,教授,博士,博士生导师,全国优秀教师,国家建材局有突出贡献的中青年专家,山东省高校十佳优秀教师。2004年1月任济南大学党委常委、副校长。现负责学校学科建设规划、科学研究与科技开发、研究生教育、网络建设与管理等方面的工作,分管科技处、研究生处、学科建设办公室、学位委员会办公室、信息网络中心。
长期从事计算机网络、智能控制与信息处理方面的教学科研工作,曾主持国家自然科学基金、国家863计划、国家科技支撑计划、国家面向21世纪教学改革项目、山东省自然科学基金、山东省自主创新重大科技专项等重要科研项目近20项,发表学术论文90多篇,其中被SCI收录10篇,EI收录30篇。近年来已培养硕士研究生30多名,培养博士研究生3名。获得山东省科技进步一等奖2项、二等奖1项,山东省计算机优秀应用成果一等奖2项,山东省优秀教学成果一等奖1项、二等奖1项,山东省高等学校优秀科研成果一等奖3项。
现为教育部高等学校计算机专业教学指导分委员会副主任委员、山东计算机学会副理事长、山东省信息化专家组副组长、山东省委保密委员会技术咨询专家组副组长、中国计算机学会青年科技工作者论坛学术委员、中国自动化学会智能控制专委会委员、山东省教育信息化专家委员会副主任委员、山东省电子商务协会副理事长。
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主页访问
1638
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关注数
0
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成果阅读
284
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成果数
6
【期刊论文】Time-series forecasting using flexible neural tree model
杨波, Yuehui Chen a, *, Bo Yang a, Jiwen Dong a, Ajith Abraham a, b
Information Sciences (2004)1-17,-0001,():
-1年11月30日
Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a timeseries model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.
Flexible neural tree model, Probabilistic incremental program evolution, Simulatedannealing, Time-series forecasting
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63浏览
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【期刊论文】Automatic Design of Hierarchical TS-FS Model Using Ant Programming and PSO Algorithm
杨波, Yuehui Chen, Jiwen Dong, and Bo Yang
C. Bussler and D. Fensel (2004)285-294,-0001,():
-1年11月30日
This paper presents an approach for designing of hierarchical Takagi-Sugeno fuzzy system (TS-FS) automatically. The hierarchical structure is evolved using Ant Programming (AP) with specific instructions. The fine tuning of the rule's parameters encoded in the structure is accomplished using Particle Swarm Optimization (PSO) algorithm. The proposed method interleaves both optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it fine tunes its rules' parameters. It then goes back to improving the structure again and, provided it finds a better structure, it again fine tunes the rules' parameters. This loop continues until a satisfactory solution (hierarchical Takagi-Sugeno fuzzy model) is found or a time limit is reached. The performance and effectiveness of the proposed method are evaluated using time series prediction problem and compared with the related methods.
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32浏览
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82下载
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【期刊论文】Feature selection and classification using flexible neural tree
杨波, Yuehui Chen a, Ajith Abraham a, b, *, Bo Yang a, c
Neurocomputing 70(2006)305-313,-0001,():
-1年11月30日
The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.
Flexible neural tree model, Genetic programming, Memetic algorithm, Intrusion detection system, Breast cancer classification
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60浏览
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101下载
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【期刊论文】Evolving Flexible Neural Networks Using Ant Programming and PSO Algorithm
杨波, Yuehui Chen, Bo Yang, and Jiwen Dong
F. Yin, J. Wang, and C. Guo (2004)211-216,-0001,():
-1年11月30日
A flexible neural network (FNN) is a multilayer feedforward neural network with the characteristics of: (1) overlayer connections; (2) variable activation functions for different nodes and (3) sparse connections between the nodes. A new approach for designing the FNN based on neural tree encoding is proposed in this paper. The approach employs the ant programming (AP) to evolve the architecture of the FNN and the particle swarm optimization (PSO) to optimize the parameters encoded in the neural tree. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with the related methods.
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37浏览
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103下载
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【期刊论文】Hybrid Neurocomputing for Breast Cancer Detection
杨波, Yuehui Chen and Ajith Abraham
,-0001,():
-1年11月30日
Breast cancer is one of the major tumor related cause of death in women. Various artificial intelligence techniques have been used to improve the diagnoses procedures and to aid the physician's effors. In this paper we summarize our preliminary study to detect breast cancer using a Flexible Neural Tree (FNT), Neural Network (NN), Wavelet Neural Network (WNN) and their ensemble combination. For the FNT model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal FNT. For the NN and WNN, the PSO is employed to optimize the free parameters. The performance of each approach is evaluated using the breast cancer data set. Simulation results show that the obtained FNT model has a fewer number of variables with reduced number of input features and without significant reduction in the detection accuracy. The overall accuracy could be improved by using an ensemble approach by a voting method.
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43浏览
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66下载
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【期刊论文】Time-series prediction using a local linear wavelet neural network
杨波, Yuehui Chen a, *, Bo Yang a, b, Jiwen Dong a
Neurocomputing 69(2006)449-465,-0001,():
-1年11月30日
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method.
Local linear wavelet neural network, Particle swarm optimization algorithm, Gradient descent algorithm, Time-series prediction
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49浏览
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84下载
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