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2012 2nd International Conference on Advanced Materials and Computer Science(ICAMCS 2012)

作者： Zhibiao Li Department of Computer Science Jiangxi University of Traditional Chinese Medicine

In this paper, artificial neural network architecture is introduced to predict the Yin-Yang index of body constitution in traditional Chinese medicine(BCTCM). With pre-processing the inputting data by the median, the collected data is more consistent with the exact value of the characteristic parameters of BCTCM. Quasi-Newton algorithm is used to train the network model to accelerate the convergence speed of network training. Experiments show that, the result showed that they had good prediction accuracies for BCTTCM. The mean absolute error for 10 true measured points was 0.034. Therefore, the prediction model of BCTCM Yin-Yang index with BP neural network is doable.

关键词： Traditional Chinese Medicine Constitution Artificial neural network back propagation algorithm Quasi-Newton algorithm

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Investigations on Wind Resource Assessment and Wind Farm Layout Using Hybrid algorithms and Simulation Tools

Investigations on Wind Resource Assessment and Wind Farm Lay...

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作者： BALAKRISHNA MOORTHY C BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE PILANI

学位级别：博士

In recent years, globally wind power development has been strongly supported by ad- vent of newer computer based techniques for prediction, assessment, optimization and forecasting. Although in recent years, accelerated wind power development has been witnessed, much larger on-shore as well as off-shore wind power potential still remains to be utilized. Wind power developers often rely on computer based models for speedy generation of accurate and reliable estimates of wind resource and wind power genera- tion at a wind farm site. Researchers have reported several efforts to develop applica- tions of modern artificial intelligence based algorithms and software tools for simula- tion of wind farms. In the present work, the focus is on applying artificial intelligence techniques to predicting rate of growth of wind power generation capacity, estimating characteristics of wind resource at a wind farm site, optimizing of position of wind tur- bine in a wind farm and fore-casting wind power generated over short duration ranging from few hours to few days. The developed techniques and results obtained provide useful insight to wind power developers for re-assessment of wind resource at a site and for re-powering of existing wind farms. The thesis is presented in eight chapters. The introduction, survey of recent relevant literature on wind resource assessment and wind farm layout, is followed by investiga- tion of application of genetic algorithm in predicting increase in cumulative installed wind power generation capacity on annual basis. The accuracy of the technique is es- tablished with respect to actually recorded growth in installed capacity, in terms of root mean square error (RMSE) of prediction. It is confirmed that accuracy of prediction depends on volume of recorded data input. Next, use of genetic algorithm in obtaining optimum values of shape (k) and scale parameters (c), required for defining character- istic Weibull probabilistic distribution function at an

关键词： Wind Resource Assessment Wind Farm Layout Prediction and Forecasting of Wind Power Generation Genetic algorithm Artificial Neural Network back propagation algorithm Windographer WindSim Meteodyn WindFarmer

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Fuzzy neural network model applied in the mine water inrush prediction

Fuzzy neural network model applied in the mine water inrush ...

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Conference on International Conference on Image Processing and Pattern Recognition in Industrial Engineering

作者： Xiao Jian-yu Tong Min-ming Fan Qi Zhu Chang-jie China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

ISBN: (纸本)9780819483294

Combining with fuzzy reasoning, neural network and back propagation algorithm, a fuzzy neural network model for the prediction of mine water inrush is built after the analysis of the main factors affected the mine water inrush, such as water pressure, fault throw, water conducted zone width, aquifer thickness and confining bed thickness. Then, on the basis of the water inrush mechanism and some practical examples, influential factors on mine water inrush are divided into three groups, and after that 81 fuzzy inference rules are constructed effectively. And then, the four-layer back propagation fuzzy neural network takes effect in training the input variables. Finally, a simulated prediction of the fuzzy neural network is made by using test samples. The results of simulation show that mine water inrush model based on fuzzy neural network is superior to the traditional BP neural network on training speed and predicting precision.

关键词： Fuzzy neural network inference rule back propagation algorithm mine water inrush water inrush prediction

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Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

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SN Computer Science 2022年第3期3卷 189页

作者： Mishra, Sandhya Soni, Devpriya Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Sector-128 Noida India

Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in ‘Smishing Detector’ model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： back propagation algorithm Cyber security Information security Mobile security Phishing

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Research on Movie Recommendation algorithm based on Deep Learning 2

Research on Movie Recommendation Algorithm based on Deep Lea...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhan, Yangfei Xie, Haozhi Huan, Huabin Che, Sumei Hunan Normal University School of Journalism and Communication Changsha China Beihang University School of Computer Science and Engineering Beijing China Jilin International Studies University International Media College Changchun China Xianghu Middle School Longyan China

ISBN: (纸本)9798350317558

In this paper, we utilize the mechanism of simulating human nervous system in deep learning for movie data. Feature extraction is performed automatically and unstructured data is processed to improve the model recommendation effect. Through the loss function of the output layer, iterative optimization is carried out with the help of the back propagation algorithm, which passes the prediction error from the output layer to the input layer in reverse, and updates the network parameters of each layer in turn. The analysis found that the accuracy of the movie recommendation algorithm based on deep learning increases faster, to the point that after 160 rounds the accuracy leveled off, compared with the optimal algorithm improved by 1.4%. The movie recommendation algorithm based on deep learning can better meet the user's personalized needs and recommend the movie that best meets the user's requirements. © 2024 IEEE.

关键词： back propagation algorithm deep learning loss function output layer prediction error

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Research on the Evaluation of Mathematical Teaching Quality Based on K-means and BP algorithm 7

Research on the Evaluation of Mathematical Teaching Quality ...

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7th International Conference on Education, Network and Information Technology, ICENIT 2024

作者： Yao, Yan Gao, Xiaocheng Shen, Qi Heihe University College of Science Heihe China

ISBN: (纸本)9798350374919

This study aims to refine the backpropagation (BP) algorithm to enhance the precision and efficiency of evaluating mathematical teaching quality. Initial data collection, facilitated by the K-means clustering method, establishes foundational structures for subsequent analyses by identifying sample cluster centers. Subsequently, meticulous determination of weights and thresholds for various metrics evaluating mathematical teaching quality refines the evaluation process, enabling a more nuanced capture of teaching effectiveness. MATLAB simulations conducted at different error thresholds (0.1, 0.01, and 0.001) consistently demonstrate the superiority of the enhanced BP algorithm over its standard counterpart, evidencing improved evaluation accuracy and efficiency. These findings closely align with anticipated educational quality objectives, validating the effectiveness of the refined evaluation approach. Consequently, the refined BP algorithm emerges as a robust tool for accurately assessing mathematical teaching quality, effectively mitigating the influence of extraneous factors on evaluation outcomes. By furnishing a comprehensive evaluation framework, this research significantly contributes to the advancement of methodologies for assessing educational effectiveness, particularly in the realm of mathematical instruction. © 2024 IEEE.

关键词： back propagation algorithm Data collection K-means clustering Mathematical education Teaching quality assessment

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Knowledge representation in electrical insulation diagnosis

Knowledge representation in electrical insulation diagnosis

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1998 International Conference on Power System Technology (POWERCON 98)

作者： Gao, N Yang, L Yan, Z Zhu, DH Tsing Hua Univ Dept Elect Engn HV Div Beijing 100084 Peoples R China

ISBN: (纸本)0780347544

Artificial Neural Network (ANN) representation method is presented and discussed in this paper. ANN representation of symbolic and numeric knowledge is realized by the improved back propagation (BP) algorithm. The method of management and organization of ANN representation is presented also. The problem that numeric knowledge is difficult to be represented is effectively solved by the network. After learning, the threshold values and weights of ANN are ensured and the diagnostic knowledge is learned. The practical examples are used to check the diagnostic results, it is shown that the ANN representation method is effective.

关键词： knowledge representation artificial neural network back propagation algorithm symbolic knowledge numeric knowledge

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A Method for the Environmental Quality Assessment of Surface Water Based on Artificial Neural Networks

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Systems Science and Systems Engineering 2000年第1期10卷 72-76页

作者： YANG Guo\|dong\+1,\ PAN Da\|feng\+2,\ FAN Ying\|fang\+3 1.Dep. of Environmental Science, Shanxi Uni., Taiyuan 030006, China 2.Shanxi Academy of Agriculture Science, Taiyuan 030031, China 3.Institute of Molecular Science, Shanxi Uni., Taiyuan 030006, C

An assessment method for the environmental quality of surface water was established based on artificial neural networks (ANN), in which different classification values were trained as learning samples. The assessment results from the major river section in Datong city indicated that the ANN model have characteristics of simple operation and distinct and quantitative expression of assessment results in comparison with the standard index method for the environmental quality assessment of surface water.

关键词： artificial neural newtorks back propagation algorithm surface water quality comprehensive assessment

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Synthetic evaluation of enterprises future profitability based on neural networks

Synthetic evaluation of enterprises future profitability bas...

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IEEE Conference on Control Applications

作者： Zhao, JG Miao, L Dongbei Univ Finance & Econ Dalian Liaoning Prov Peoples R China

ISBN: (纸本)078037729X

After analyzing the advantages and disadvantages of common multi-target synthetic evaluation in determining enterprises future profitability, this paper gives the method of multi-target synthetic evaluation based on neural networks and applies it to the evaluation of enterprises future profitability. The simulation result demonstrates that this method is satisfactory.

关键词： neural networks enterprise future profitability back propagation algorithm synthetic evaluation

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Rozpoznávání objektů pomocí neuronových sítí

Rozpoznávání objektů pomocí neuronových sítí

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作者： Marák, Jaroslav Brno University of Technology

Tato práce se zaměřuje na neuronové sítě a jejich klasifikační schopnosti při rozpoznávání objektů v obraze. Pro rozpoznávání je zde použito vícevrstvé dop... 详细信息

Tato práce se zaměřuje na neuronové sítě a jejich klasifikační schopnosti při rozpoznávání objektů v obraze. Pro rozpoznávání je zde použito vícevrstvé dopředné neuronové sítě, trénovatelné pomocí algoritmu zpětného šíření chyby - back propagation. V práci je zmíněna problematika volby topologie takovéto sítě a významných parametrů ovlivňujících průběh učení dopředné sítě. Pomocí série experimentů rozpoznávání objektů v různých úlohách jsou prezentovány dosažené výsledky spolu se zhodnocením úspěšnosti.

关键词： Neuron dopředná neuronová síť RBF LVQ SOM algoritmus back propagation předzpracování obrazu segmentace obrazu rozpoznávání objektů. Neuron feedforward neural network RBF LVQ SOM back propagation algorithm image preprocessing image segmenta

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