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Classification Analysis of Topographical Features Using Artificial Neural Network

Procedia Technology 2016年 25卷 399-404页

作者： R. Krishnan Andhe Dharani Research Scholar VTU Research Centre Department of MCA R.V College of Engineering BangaloreKarnataka-560059 India Professor Department of MCA R V College of Engineering Bangalore Karnataka-560059 India

Identification and classification of the topographical features is a challenging topic in the field of image pattern recognition. Improvement is required in the existing crater detection algorithms because of the pattern types and complexity. Currently more than 500 images are transmitted to earth with a resolution of 5 to 100 meters. The artificial neural network plays an important role in training and classification of image patterns. This paper deals with analysis of crater detection with back propagation algorithm with training and classification, and analysis of execution time for classification of craters.

关键词： crater detection backpropagation algorithm artificial neural network

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Neural Nets as another Paradigm for Decision Support *

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IFAC Proceedings Volumes 1995年第7期28卷 117-125页

作者： Feng Shan Cai Jun Zhai Fan Huazhong University of Science and Technology Department of Automatic Control Engineering Institute of Systems Engineering Wuhan Hubei PRC 430074

The paper presents the second generation artificial neural nets ANN II applied as another paradigm for decision support. The underlying decision selection task is comprehensive evaluation of the social—economic development level of Chinese large cities, which previously supported by conventional knowledge-based approach KB-CEDSS. With the KB-CEDSS generated input-output pairs, the ANNs can be trained to emulate the whole systems function. Some foundamental technologies of ANN design and implementation are discussed and several points on ANN applications are concluded.

关键词： Group Multicriterion Decision Making GMDM Assessment and Evaluation Knowledge-based Systems Neural nets and configuration Decision Support System backpropagation algorithm

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Particle Swarm Optimization trained recurrent neural network for voltage instability prediction

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Journal of Electrical Systems and Information Technology 2018年第2期5卷 216-228页

作者： Amr M. Ibrahim Noha H. El-Amary Electrical Power and Machines Engineering Dept. Ain Shams University Cairo Egypt Electrical Power and Control Engineering Dept. Arab Academy for Science and Technology Egypt

Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent neural network trained with Particle Swarm Optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. Also, a detailed comparison between PSO algorithm and backpropagation (BP) algorithm is discussed. The results proved the effectiveness of the proposed method.

关键词： backpropagation algorithm Particle Swarm Optimization technique Recurrent neural network Voltage instability predictor Voltage stability

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Prediction of natural gas hydrates formation using a combination of thermodynamic and neural network modeling

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 182卷 106270-000页

作者： Rebai, Noura Hadjadj, Ahmed Benmounah, Abdelbaki Berrouk, Abdallah S. Boualleg, Salim M. Univ MHamed BOUGARA Boumerdes Fac Sci Ingenieur UR MPE Ave Independance Boumerdes 35000 Algeria Algerian Petr Inst Boumerdes 35000 Algeria Khalifa Univ Sci & Technol Petr Inst Mech Engn Dept POB 2533 Abu Dhabi U Arab Emirates

During the treatment or transport of natural gas, the presence of water, even in very small quantities, can trigger hydrates formation that causes plugging of gas lines and cryogenic exchangers and even irreversible damages to expansion valves, turbo expanders and other key equipment. Hence, the need for a timely control and monitoring of gas hydrate formation conditions is crucial. This work presents a two-legged approach that combines thermodynamics and artificial neural network modeling to enhance the accuracy with which hydrates formation conditions are predicted particularly for gas mixture systems. For the latter, Van der Waals-Platteeuw thermodynamic model proves very inaccurate. To improve the accuracy of its predictions, an additional corrective term has been approximated using a trained network of artificial neurons. The validation of this approach using a database of 4660 data points shows a significant decrease in the overall relative error on the pressure from around 23.75%-3.15%. The approach can be extended for more complicated systems and for the prediction of other thermodynamics properties related to the formation of hydrates.

关键词： Gas hydrate formation Van der Waals-Platteeuw models Artificial neural network backpropagation algorithm

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A learning algorithm with activation function manipulation for fault tolerant neural networks

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2001年第7期E84D卷 899-905页

作者： Kamiura, N Taniguchi, Y Hata, Y Matsui, N Himeji Inst Technol Dept Comp Engn Himeji Hyogo 6712201 Japan

In this paper we propose a learning algorithm to enhance the fault tolerance of feedforward neural networks (NNs for short) by manipulating the gradient of sigmoid activation function of the neuron. We assume stuck-at-0 and stuck-at-1 faults of the connection link. For the output layer, we employ the function with the relatively gentle gradient to enhance its fault tolerance. For enhancing the fault tolerance of hidden layer, we steepen the gradient of function after convergence. The experimental results for a character recognition problem show that our NN is superior in fault tolerance, learning cycles and learning time to other NNs trained with the algorithms employing fault injection, forcible weight limit and the calculation of relevance of each weight to the output error. Besides the gradient manipulation incorporated in our algorithm never spoils the generalization ability.

关键词： feedforward neural network backpropagation algorithm stuck-at fault sigmoid activation function

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Traceability analysis for low-voltage distribution network abnormal line loss using a data-driven power flow model

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Sun, Zhiqing Xuan, Yi Huang, Yi Cao, Zikai Zhang, Jiansong State Grid Zhejiang Elect Power Co Hangzhou Power Supply Co Hangzhou Peoples R China State Grid Corp China Beijing Peoples R China Shanghai Univ Elect Power Sch Elect Power Engn Shanghai Peoples R China State Grid Zhejiang Elect Power Co LTD Hangzhou Peoples R China

The abnormal behavior of end-users is one of the main causes of abnormal line loss in distribution networks. The integration of a large amount of distributed renewable energy into a low-voltage distribution network (LVDN) complicates line loss analysis. Traceability analysis for abnormal line loss aims to identify the specific end-user responsible for the anomaly in line loss. This paper proposes, for LVDNs with incomplete topology and line parameters, a practical traceability analysis approach using a data-driven power flow model. A data-driven power flow model based on a neural network is first established to capture the power flow mapping relationship without topology and line parameter information. A backpropagation algorithm is then presented to correct the actual power consumption data according to the measured voltage data. By comparing actual power consumption data with measured power data, users with abnormal behavior can be accurately identified and tracked. Finally, the effectiveness of the proposed approach is verified by actual data.

关键词： traceability analysis abnormal line loss data-driven power flow model backpropagation algorithm low-voltage distribution network

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Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days

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BUILDINGS 2022年第5期12卷 513页

作者： Xie, Yuquan Hu, Wen Zhou, Xilin Yan, Shuting Li, Chuancheng Wuhan Univ Technol Sch Civil Engn & Architecture 122 Luoshi Rd Wuhan 430070 Peoples R China Tohoku Univ Grad Sch Engn Dept Architecture & Bldg Sci Sendai Miyagi 9808579 Japan Wuhan Inst Technol Sch Civil Engn & Architecture 693 Xiongchu Rd Wuhan 430205 Peoples R China

In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 degrees C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings.

关键词： backpropagation algorithm sensitivity analysis outdoor thermal environment mean radiant temperature genetic algorithms

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A Recurrent Neural Network Model of the B.t. Fed-Batch Fermentation Process 1

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IFAC Proceedings Volumes 2001年第5期34卷 385-390页

作者： Barrera-Cortés J. Baruch I. Valdez-Castro L. Vázquez-Cervantes V. Department of Biotechnology and Bioengineering Department of Automatic Control Avenida Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco A.P. 14-470 México D.F. Mexico. C.P. 07360 Fax: (+52-5)747-38-00 ext. 4305

This paper proposes the use of a new Recurrent Trainable Neural Network model (RTNN) for modeling the fed-batch fermentation kinetics data of Bacillus thuringiensis as a function of the operational conditions. The proposed RTNN model has ten inputs, six outputs and sixteen neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the backpropagation through time. The approximation error is below 2% and the generalization error is below 6%. The learning was performed in 101 epochs, 92 iterations each one. The RTNN model validation was done with experimental data not included in the learning process.

关键词： Neural networks process identification backpropagation algorithm fermentation processes

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Identification of Plant Growth Under DIF Condition

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IFAC Proceedings Volumes 1997年第11期30卷 1709-1714页

作者： Hiroshi Shimizu Minoru Yamazaki Faculty of Agricultural Engineering Kyoto University Kitashirakawa Oiwake-cho Sakyo-ku Kyoto 606-01 JAPAN

A neural network was used to identify the stem elongation of a plant The network architecture was three layers network, input layer, hidden layer and output layer. The input data were environmental conditions, DIF and photoperiod, for plant growth, and the output data were coefficients of certain equation that represents plant stem elongation. The network was trained using the backpropagation algorithm. An experiment for measuring the stem elongation of a plant was conducted to collect data for verifying the network output.

关键词： Agriculture Identification Neural networks backpropagation algorithm Models

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Continuous–Discrete Time Neural Network Observer for Nonlinear Dynamic Systems Application to Vehicle Systems

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IFAC-PapersOnLine 2023年第2期56卷 5685-5690页

作者： Hasan Abdl Ghani Hind Laghmara Sofiane Ahmed Ali Samia Ainouz Xing Gao Redouane Khemmar Normandie Univ UNIROUENESIGELEC Laboratoire IRSEEM 76800 Saint-Etienne-du-Rouvray France Normandie Univ INSA Rouen LITIS 76800 Saint-Etienne-du-Rouvray France IBISC Evry-Val-d'Essonne University Universite Paris-Saclay Evry France

This paper proposes a novel continuous-discrete (sampled data) time neural network (NSNN) observer for nonlinear systems. It can therefore be applied to systems with a high degree of non-linearity with no prior knowledge of the system dynamics. The proposed observer is a three-layer feedforward neural network that has been intensively trained using the error backpropagation learning algorithm, which includes an e-modification term to ensure robustness of the observer. A structure of the output predictor with a corrective term is added in the structure of the NN observer to overcome the problem of discrete time measurement. Simulations using MATLAB and CarSim are illustrated to demonstrate the performance of the proposed state observer strategy to reconstruct the state variables and parameters of a vehicle system.

关键词： Sampled data neural network Nonlinear observer backpropagation algorithm Vehicle systems

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