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2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Wei Gao Nan Jiang Jingxin Wen Hai Zhao Institute of Information and Technology Northeastern University Shenyang Institute of Chemical Techn Shenyang Institute of Chemical Technology Shenyang China Institute of Information and Technology Northeastern University Shenyang China

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and bp neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough set theory is first used to process the sample data, and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the improved algorithm is demonstrated by an example compared with the traditional neural network system.

关键词： rough set neural network bp algorithm data fusion attribute reduction

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The Wavelet and PSO-bp Neural Network Based Analysis of Financial Time Series

The Wavelet and PSO-BP Neural Network Based Analysis of Fina...

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第六届（2014）金融风险与公司金融国际研讨会

作者： He Yang Li Zheng School of Management Harbin Institute of Technology School of Electrical Engineering Harbin Institute of Technology

Financial time series are nonlinear,non-stationary and have a long memory,which make it difficult to achieve the desired prediction *** this paper,we propose a forecasting method,combining the wavelet analysis and the particle swarm optimization(PSO) neural *** use the wavelet analysis to de-noise the non-stationary time series,and then employ bp neural network based on PSO to predict the time series after *** paper researches the selection criteria of a wavelet function,forecasts the time series of Shanghai Composite Index closing prices with wavelet analysis and compares the results with traditional prediction *** is obvious that after wavelet de-noising,the same neural network analysis increases the forecasting prediction accuracy by almost an order of magnitude and the prediction error is much smaller than the prediction error without wavelet *** method improves the hysteretic nature and can achieve the ideal *** facilitates the analysis of the characteristics of financial risks and financial markets.

关键词： wavelet analysis wavelet de-noising PSO bp algorithm time series forecasting

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The research on an algorithm for special two-hidden-layer artificial neural networks

The research on an algorithm for special two-hidden-layer ar...

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International Conference on Machine Learning and Cybernetics

作者： Ren, JD Huang, HY Bao, R Yanshan Univ Qinhuangdao 066004 Peoples R China

ISBN: (纸本)0780378652

Generalization ability of the network and training time are the two important aspects that we must consider when we design the neural network algorithms. At the same time, the optimization of neural network architecture must be considered in each artificial neural network based on the bp algorithm. But to the larger networks, there are no more suitable ways to solve this problem. This paper proposes an especial two-hidden-layer artificial neural network. After describing the major steps of this algorithm, some experimental results and analysis are given out. Those experimental results indicate that the generalization ability, training time and the architecture optimization of the networks have been improved obviously in this algorithm.

关键词： bp algorithm training time two-hidden-layer

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Research on Big Data in Business Model Innovation Based on GA-bp Model

Research on Big Data in Business Model Innovation Based on G...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Wenbo Jiang Huaqi Chai School of management Northwestern Polytechnical University

Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, data mining perspectives are pointed out about how business model innovation is driven by "key data" based on illustrations about big data. Nine basic factors can be represented as business model innovation. GA-bp model is constructed by the combination of genetic algorithm and bp algorithm to extract the knowledge from the data in data mining environment and to find associations, patterns by analyzing the big data sets. Finally, "key data" that affects the consequence significantly can be grabbed to explore entry points for business model innovation in the era of big data, and to offer enterprises and executives for business model innovation from a new version.

关键词： big data business model innovation GA-bp model data mining Genetic algorithm bp algorithm

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Study on Stock Price Prediction Based on bp Neural Network

Study on Stock Price Prediction Based on BP Neural Network

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2010 IEEE International Conference on Emergency Management and Management Sciences(2010 IEEE应急管理与管理科学国际会议 ICEMMS)

作者： Ningfang Dong Weimin Ma Yingying Wang School of economics and management Tongji University Shanghai China School of economics and management Tongji University Shanghai China

In this paper,two kinds of methods, namely additional momentum method and self-adaptive learning rate adjustment method, are used to improve the bp algorithm. Considering the diversity of factors which affect stock prices, Single-input and Multi-input Prediction Model (SIPM and MIPM) are established respectively to implement short-term forecasts for SDIC Electric Power (600886) shares and Bank of China (601988) shares in 2009. Experiments indicate that the improved bp model has superior performance to the basic bp model, and MIPM is also better than SIPM. However, the best performance is obtained by using MIPM and improved prediction model cohesively.

关键词： Stock prediction bp algorithm Neural network MSE

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GPU-Based Fast Imaging Scheme for GB-MIMO Radar

GPU-Based Fast Imaging Scheme for GB-MIMO Radar

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2021 CIE International Conference on Radar, Radar 2021

作者： Hou, Sican Ren, Xiaoyu Tian, Weiming Deng, Yunkai Dong, Xichao Beijing Institute of Technology Key Laboratory of Electronic and Information Technology in Satellite Navigation Ministry of Education Beijing China Beijing China Radar Research Lab Beijing China

ISBN: (纸本)9781665498142

GB-MIMO (Ground-Based Multiple Input Multiple Output) radar plays an important role in landslide disaster prevention. Common MIMO radar imaging based on bp (Back Projection) algorithm is slow, and severely affects the real-time performance of deformation measurement. In this paper, a fast imaging scheme applied for GB-MIMO radar is proposed based on a heterogeneous system consisting of CPU (Center Processing Unit) and GPU (Graphic Processing Unit). The signal processing architecture is firstly designed according to the radar imaging principle and algorithm analysis. Then the acceleration technology of GPU is studied with CUDA technology. The results show that the computing speed achieves 30∼35 times in three different echo sizes less than conventional schemes, which proves that the method can meet the requirement of real-time well. © 2021 IEEE.

关键词： bp algorithm Fast Imaging GB-MIMO Radar GPU

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A NEURAL NETWORK-BASED MODEL FOR PREDICTION OF HOT-ROLLED AUSTENITE GRAIN SIZE AND FLOW STRESS IN MICROALLOY STEEL

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Acta Metallurgica Sinica(English Letters) 2000年第2期13卷 521-530页

作者： J. T.Niu,L.J.Sun and P.Karjalainen 1) Harbin Institute of Technology, Harbin 150001, China 2) University of Oulu, FIN-90571, Oulu, Finland

For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of bp algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.

关键词： microalloy steel controlled rolling austenite grain size flow stress neural network bp algorithm

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The Design and Application of Control System Based on the bp Neural Network

The Design and Application of Control System Based on the BP...

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2015 3rd International Conference on Mechanical Engineering and Intelligent Systems(ICMEIS 2015)

作者： Xinglei Li Hongbin Yu School of Mechanical Engineering Tianjin Polytechnic University

Artificial neural network is composed by a large number of processing interconnected unit. It is a nonlinear, adaptive information processing system. It has self-organizing, adaptive and self-learning ability. And can be used to calculate complex relationship between input and output, thus it has effective control ability. Rubber is the main material in the process of the mixer. It has a great influence on the final product. But at present many factories are added in the rubber by manual control. So this article takes the rubber transport equipment as the object, establish a bp neural network intelligent control system, using neural network self-learning ability and the adaptive ability to deal with uncertain information, to solve the problem of motion control of multiple dynamic input. Through the test of practical application, the system has strong robustness, adaptability, good generality and fault tolerance.

关键词： mixer process Neural network bp algorithm Asynchronous motor

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Character Recognition Based on bp Artificial Neural Network

Character Recognition Based on BP Artificial Neural Network

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2011 International Conference on Information Management and Engineering(ICIME 2011)

作者： Bin xiao Weilin Peng School of Information Beijing Forestry UniversityComputer and Network CenterNorth China University of Technology School of Information North China University of Technology

This paper provides a character recognition system based on the Back Propagation artificial neural network that is simplified. Trained with samples of the characters, the characters are recognized, using with VC++. Re... 详细信息

ISBN: (纸本)9781612842387

关键词： character recognition artificial neural networks bp algorithm

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Forecasting Sedimentation of constructions Based on bp Network

Forecasting Sedimentation of constructions Based on BP Netwo...

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2009 International Conference on Environmental Science and Information Application Technology

作者： JIANG Ting-chen School of Geodesy&Geomatics Engineering Huaihai Institute of Technology Lianyungang,China

If sedimentation of constructions exceeds the prescribed limits,it would give rise to huge losses for community and people,so it is significant to establish the effective and practical deformation forcasting model for the safe operation and economic *** the unique non-linear,non-convexity,non-locality,non-steadiness,adaptability and powerful ability of calulation and information process,bp(Back Propagation)neural network can adapt to the complicated and changeable dynamic characteristics of buildings,that has broad application foreground in deformation *** this paper,on the basis of deformation observation data,a basic algorithm about establishing bp neural network model in sedimentation prediction is *** the same time,analysis of examples are gived so that the application of neural network for deforamtion forcasting is studied comprehensively and systemically,the results show that neural network is very effective for Sedimentation prediction and can serve society and people in the future.

关键词： bp Neural networks sedimentation Forecast bp algorithm

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