检索结果-内蒙古大学图书馆

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and data preprocessing techniques

引用

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第2期12卷 911-917页

作者： Huang, Wei Oh, Sung-Kwun Univ Suwon Dept Elect Engn Suwon South Korea Tianjin Univ Technol Sch Comp & Commun Engn Tianjin Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function;2) lots of parameters in the design of classifier;and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

关键词： Polynomial Neural Network Classifier (PNNC) Principal component analysis Linear discriminate analysis Space search optimization Simultaneous tuning strategy data preprocessing technique

来源：评论

学校读者我要写书评

暂无评论

A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting

引用

APPLIED ENERGY 2018年 215卷 643-658页

作者： Song, Jingjing Wang, Jianzhou Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Software Sydney NSW Australia

Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.

关键词： Wind speed forecasting Combined model data preprocessing technique Advanced optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis

引用

NEUROCOMPUTING 2018年 303卷 20-36页

作者： Kim, Eun-Hu Oh, Sung-Kwun Pedrycz, Witold Univ Suwon Dept Elect Engn San 2-2 Wau Ri Hwaseong Si 445743 Gyeonggi Do South Korea Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst Warsaw Poland King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

This paper is concerned with a new architecture of a reinforced hybrid interval fuzzy neural networks (RHIFNN) classifier developed with aid of Fuzzy C-Means (FCM) clustering and Particle Swarm Optimization (PSO). The key objectives of this study concern the following: (a) selection of preprocessing techniques for the dimensionality reduction of input space. Linear discriminant analysis (LDA) or principal component analysis (PCA) algorithm forms a front end of the network to form the low-dimensional input variables. (b) The efficient process of dealing with uncertain information by interval type-2 fuzzy sets using Fuzzy C-Means (FCM) clustering. The premise (condition) part of the rules is realized by two FCM clustering algorithms, which are invoked by using different values of the fuzzification coefficient subsequently resulting in interval-valued type-2 membership functions. (c) The simultaneous structural and parametric optimization of network by evolutionary algorithm is completed. The parameters of the network including both the premise and consequent parts are optimized by means of the particle swarm optimization (PSO). The proposed classifier is applied to a variety of machine learning datasets and the results are compared with those provided by other classifiers reported in the literature.

关键词： data preprocessing technique Fuzzy c-means Interval fuzzy neural networks (ifnn) Linear discriminant function Particle swarm optimization Principal component analysis Type-2 fuzzy set

来源：评论

学校读者我要写书评

暂无评论

Determination of tissue optical properties from spatially resolved relative diffuse reflectance by PCA-NN

Determination of tissue optical properties from spatially re...

引用

International Conference on Neural Networks and Signal Processing

作者： Chen, YQ Lin, L Li, G Ye, WY Yu, QL Tianjin Univ Coll Precis Instruments & Optoelect Engn Tianjin 300072 Peoples R China

ISBN: (纸本)0780377028

Noninvasive determination of tissue optical properties is essential for clinical applications in medical diagnostics and therapeutics. In this paper, we describe a method to determine reduced scattering coefficient mu(s)' and absorption coefficient mu(a) from spatially resolved relative diffuse reflectance. A neural network in conjunction with data preprocessing technique - principal component analysis (PCA) is employed to perform the estimations from the diffuse reflectance data generated by Monte Carlo simulations. The PCA-NN was trained and tested on the space with mu(s)' between 0.1 and 2.0 mm(-1) and mu(a) between 0.01 and 0.1 mm(-1). Tests on the above space show the rms errors of this method to be 4.6% for mu(s)' and 9.2% for mu(a). It's a more efficient and robust way for real-time clinical applications than other methods.

关键词： neural nets principal component analysis Monte Carlo methods medical diagnostic computing biological tissues tissue optical properties spatially resolved relative diffuse reflectance PCA-NN medical diagnostics therapeutics scattering coefficient reduction absorption coefficient neural network data preprocessing technique principal component analysis Monte Carlo simulations real-time clinical applications

来源：评论

学校读者我要写书评

暂无评论

A new combined model based on multi-objective salp swarm optimization for wind speed forecasting

引用

APPLIED SOFT COMPUTING 2020年 92卷 106294-106294页

作者： Cheng, Zishu Wang, Jiyang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

Wind energy as the representative renewable energy sources attracted the global attention and wind power plays a significant role in power system. Thus, wind speed forecasting is highly critical in wind power grid management. The short-term wind speed prediction can effectively support power grid-management to reduce wind curtailments. In the past, lots of researches had often considered how to enhance the accuracy or stability in short wind speed forecasting. Nevertheless, just focus on one criterion is the inability to build an effective predictive system. In this paper, a novel combined forecasting system was proposed and effectively applied to address the issue of wind speed prediction while obtaining high precision and strong stability simultaneously at the same time. Four ANNs (artificial neural networks) were combined by the optimal weighting coefficients determined by MSSO (multi-objective salp swarm optimizer) in this system and data decomposition and denoising are included in the data preprocessing stage. The multi-objective optimization algorithm overcomes the weakness of the single-objective optimization algorithm that can only achieve one criterion. It can simultaneously optimize accuracy and stability. The 10-minute wind speed data of three data sets of Penglai, China were selected for multi-step forecasting to evaluate the effectiveness of the proposed combined model. And experimental results show that the proposed model not only achieves excellent precision and stability but also outperforms other proposed combined models. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Combined model Wind speed forecasting Multi-objective optimization algorithm data preprocessing technique

来源：评论

学校读者我要写书评

暂无评论

Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

引用

ENERGIES 2019年第10期12卷 1931页

作者： Zhang, Yechi Wang, Jianzhou Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sch Software Fac Engn & Informat Technol Sydney NSW 2007 Australia

Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance;however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model's effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

关键词： electric load forecasting data preprocessing technique multiobjective optimization algorithm combined model

来源：评论

学校读者我要写书评

暂无评论

A novel combined model based on hybrid optimization algorithm for electrical load forecasting

引用

APPLIED SOFT COMPUTING 2019年 82卷 105548-000页

作者： Wang, Rui Wang, Jiyang Xu, Yunzhen Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Lanzhou Univ Sch Basic Med Sci Lanzhou 730000 Gansu Peoples R China

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people's daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to suboptimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Electrical load forecasting Combined model data preprocessing technique Classical forecasting methods Hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel combined forecasting system based on advanced optimization algorithm-A study on optimal interval prediction of wind speed

引用

ENERGY 2023年 264卷

作者： Li, Jingrui Wang, Jiyang Li, Zhiwu Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Wind speed forecasting is becoming increasingly crucial for an environmentally friendly and sustainable economy because of the renewability and benefits of wind energy. Currently, many scholars have proposed various approaches to forecasting wind speed;however, due to the limitations of point prediction and drawbacks of traditional individual methods, it is challenging to acquire satisfactory results. Compared with the previous articles, this paper combines a data preprocessing strategy by fuzzification, interval estimation, and advanced optimization methods to enhance the accuracy of wind speed forecasting. Moreover, the weight coefficient allocated by a multi-objective algorithm is proved to reach Pareto optimal solution in theory. Based on the comparative experiments and discussion of the performance of the developed combined forecasting system and other control groups, it is revealed that the combined system not only outperforms for predicting wind speed with higher accuracy and stability but also enables a valid assessment of uncertainty than the former studies. It can be convinced that the developed predicting system is an appropriate and efficient tool for further practical applications in energy systems.

关键词： Wind speed forecasting data preprocessing technique Combined system Whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：