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4th International Conference on Computer Science and Education

作者： Ma, Zhuo Lin, Kunhui Jiang, Xiangmin Zhou, Changle Liu, Han Xiamen Univ Software Sch Xiamen 361005 Fujian Peoples R China Xiamen Univ Dept Comp Sci Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9781424435197

The forecasting of the corrosion of refinery's steel equipments shows great importance in preventing the accident. Considering the numerous factors affecting the corroding of refinery's steel equipments, which are uneasily predictable and with complex relationships, this paper proposed a new technology based on the bp neural network technology used in forecasting of the corrosion of refinery's steel equipments. A new model is also built and implemented in this paper. Finally, the experimental results prove the feasibility of the new model and the forecasted results by this new model fixes well with the sample data set.

关键词： neural network bp algorithm corrosion

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The Research of Complex bp Neural Network PID Control

The Research of Complex BP Neural Network PID Control

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Xue, Yang Ye, Jian-hua Qian, Hong Yang, Xu-hong Shanghai Univ Elect Power Fac Elect & Automat Engn Shanghai 200090 Peoples R China

ISBN: (纸本)9780769538167

\In this paper, a new kind of intelligent PID control method based on bp neural network is presented and a complex neural network PID controller is designed. The NN controller has strong self-adaptability and self-learning abilities. Experimental results on different complex objects prove that, the control method has better performances than traditional PID controller. The system using neural network has high control accuracy, strong adaptability and good control results.

关键词： Artificial neural network bp algorithm PID control

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Prediction model of arc furnace based on improved bp neural network

Prediction model of arc furnace based on improved BP neural ...

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International Conference on Environmental Science and Information Application Technology (ESIAT 2009)

作者： Hui, Zhao Wang, Xiaobo Wang, Xiaotao Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin Peoples R China Xihua Univ Elect & informat coll Chengdu Peoples R China

ISBN: (纸本)9780769536828

It's very important to control the electrode current of arc furnace. This paper firstly discuss intelligent control method of arc furnace based on neural network, then the three-phase current prediction model of arc furnace has been built and it is analyzed and simulated by matlab software. The result shows that the electrode is effective controlled and the effect by using improved bp method is satisfactory.

关键词： neural network arc furnace prediction model bp algorithm

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A Study of Data Fusion Based on Combining Rough Set with bp Neural Network

A Study of Data Fusion Based on Combining Rough Set with BP ...

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9th International Conference on Hybrid Intelligent Systems (HIS 2009)

作者： Gao, Wei Wen, Jingxin Jiang, Nan Zhao, Hai Northeastern Univ Shenyang Inst Chem Technol Inst Informat & Technol Shenyang Peoples R China Northeastern Univ Shenyang Inst Chem Technol Shenyang Peoples R China Northeastern Univ Inst Informat Technol Shenyang Peoples R China

ISBN: (纸本)9780769537450

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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Forecasting Sedimentation of constructions Based on bp Network

Forecasting Sedimentation of constructions Based on BP Netwo...

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International Conference on Environmental Science and Information Application Technology (ESIAT 2009)

作者： Jiang Ting-chen Huaihai Inst Technol Sch Geodesy & Geomat Engn Lianyungang Peoples R China

ISBN: (纸本)9780769536828

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 development. With 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 prediction. In this paper, on the basis of deformation observation data, a basic algorithm about establishing bp neural network model in sedimentation prediction is presented at 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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Modeling and Simulation of Screw Axis Based on PSO-bp Neural Network and Orthogonal Experiment

Modeling and Simulation of Screw Axis Based on PSO-BP Neural...

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2nd International Symposium on Computational Intelligence and Design

作者： You Zhang-ping Li Sheng-yu Li Wan-li Li Zi-guang Tongji Univ Coll Mech Engn Shanghai 200092 Peoples R China Lishui Univ Coll Mech Engn Lishui Peoples R China Changsha Univ Sci & Technol Changsha Hunan Peoples R China

ISBN: (纸本)9780769538655

It is a trouble thing to build theoretical model for stir characteristics of screw axis with variable diameters and different pitches, so a called PSO-bp Neural Network (NN) model was employed. In this mode, Particle Swarm Optimization (PSO) algorithm is used to train weights and thresholds of artificial neural network instead of bp algorithm, to overcome drawbacks of bp algorithm. To avoid the slow search speed around global optimum in the PSO-bp algorithm, a heuristic way was adopted to give a transition from particle swarm search to gradient descending search. To validate the model, a group of orthogonal experiments were designed and performed, and simulation experiment was carried out with the NN model. Simulation and experiment results indicate that PSO-bp NN is an effective training algorithm, and it provides an effective modeling approach of screw axis with variable diameters and different pitches.

关键词： particle swarm optimization bp algorithm modeling simulation orthogonal experiment neural network

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The Forward NN-PID Controllers Based on Chaos PSO-bp Hybrid Optimization algorithms for Decoupling Control System of Ball Mill

The Forward NN-PID Controllers Based on Chaos PSO-BP Hybrid ...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Cheng Qiming Cheng Yinman Guo Ruiqing Zheng Yong Shanghai Univ Elect Power Coll Elect Power & Automat Shanghai Peoples R China

ISBN: (纸本)9780769538167

As to the mules-variable, close coupling, nonlinear and time-varying characteristics of the ball mill pulverizing system, the forward NN-PID controller based on chaos PSO-bp hybrid optimization algorithms for decoupling control system of ball mill was proposed. In this controller, the control parameters of PID controller are adaptively adjusted by forward NN, the weights of NN are optimized by the mixed learning methods integrating the offline PSO algorithm combined with chaos strategies of global searching ability, with the online bp algorithm of local searching ability. The results of simulation of Matlab/Simulink show that the hybrid optimization algorithms could solve some problems effectively,such as bp algorithm or PSO algorithm with slow convergence rate and falling to partial minimum easily, and the new control method has better quality than the traditional PID decoupling control method, it has fast tracking ability, strong robustness, good decoupling ability, and it can solve the time-varying problem and the coupling problem of ball mill effectively.

关键词： ball mill chaos PSO algorithm pre-maturing bp algorithm forward neural network PID controller

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Effective prediction of DEA model by neural network

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Journal of Harbin Institute of Technology(New Series) 2009年第5期16卷 683-686页

作者：孙佰清董靖巍 Economics and Management School Harbin Institute of Technology

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training *** SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for bp *** training speed is much faster and its forecasting precision is much better than those of bp *** numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.

关键词： multi-layer neural network single parameter dynamic searching algorithm bp algorithm DEA forecasting

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Artificial Neural Networks with Ant Colony Optimization for Assessing Performance of Residential Buildings

Artificial Neural Networks with Ant Colony Optimization for ...

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International Conference on Future BioMedical Information Engineering

作者： Shi, Huawang Li, Wanqing Hebei Univ Engn Sch Civil Engn Handan 056038 Hebei Peoples R China Hebei Univ Engn Coll Econ & Management Hebei 056038 Peoples R China

ISBN: (纸本)9781424446902

This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural network, the bp algorithm has good local performance but it is easy to fall into local Minimum, and the ant colony algorithm has good global performance, so the following combinatorial method is put forward. Then, the neural network is trained based on ant colony algorithm (ACbp algorithm) in global space, the parameters of neural network is trained using bp algorithm in local space. At last, a case study carried out on the performance assessment of sample residential buildings using the model shows that the ACbp neural network outperforms bp neural network and AC neural network in the aspect of dynamic error forecast is verified by computer emulation example, and related conclusions are given.

关键词： neural network ant colony algorithm bp algorithm residential buildings performance assessment

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A Multi-level Artificial Neural Network for Gasoline Demand Forecasting of Iran

A Multi-level Artificial Neural Network for Gasoline Demand ...

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2nd International Conference on Computer and Electrical Engineering

作者： Kazemi, A. Shakouri G, H. Mehregan, M. R. Taghizadeh, M. R. Menhaj, M. B. Foroughi.A, A. Univ Tehran Tehran Iran Amirkabir Univ Technol Tehran Iran Natl Iranian Oil Co Tehran Iran

ISBN: (纸本)9780769539256

This paper presents a neuro-based approach for Iran annual gasoline demand forecasting by several socioeconomic indicators. In order to analyze the influnce of economic and social indicators on the gasoline demand, the gross domestic product (GDP), the population and the total number of vehicles are selected. This approach is structured as a multi-level artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with the backpropagation (bp) algorithm. This multi-level ANN is designed properly. Actual data of Iran from 1968-2006 is used to train the multi-level ANN and illustrate capability of the approach in this regard. Comparison of the model predictions with data of the evaluating period shows validity of the model. Furthermore, the demand for the period of 2007 to 2030 is estimated.

关键词： ANN MLP bp algorithm Forecasting Gasoline demand

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