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Journal of the Institution of Engineers (India), Part CP: Computer Engineering Division 2007年第MAY期88卷 22-24页

作者： Selvan, P.T. Suganthi, S. Department of Electronics and Communication Engineering Trichy Engineering College Somu Nagar Trichy 621 132 India

In the present scenario, CAD design for circuit components is an useful task. This paper deals with efficient software implementation of neural network in MATLAB environment with simulink to characterize coplanar wa veguide (CPW). This involve the task of implementation of perception neural network using back propagation algorithm until the simulation of the network using MATLAB. The back propagation algorithm used here has memory reduction feature and is the fastest training algorithm for the networks of moderate size. CPWis the one of the very few choices for using in millimeter wave frequency applications.

关键词： Artificial neural network back propagation algorithm Coplanar waveguide

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MACHINE LEARNING BASED TECHNIQUE TO LEARN HIPPOCAMPAL ATROPHY FROM AXIAL MRI FOR ALZHEIMER?S DISEASE DIAGNOSIS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2022年第3期23卷 81-90页

作者： Ramkumar, D. K. Balaji, N. V. Genish, T. Karpagam Acad Higher Educ Dept Comp Sci Coimbatore India KPR Coll Arts Sci & Res Coimbatore India

Hippocampus (HC) is one of the small brain components and its features majorly take part in diagnosing diseases such as Alzheimer and Dementia. The earlier detection of the size changes of HC leads to take preventive action against Alzheimer disease at initial stage. Thus the HC voxel quantification becomes essential to know the severity of the disease and thus induces computerized segmentation process. Several semi-automatic and automatic HC segmentation techniques proposed earlier. Though, it requires large memory space and high computational cost. This paper reduces the risk of searching a high configuration machine and reduces the cost by utilizing limited number of features. It is to be done by using some strategic features based on mathematical framework of wavelet, statistical features and gray level computations called level set. The features fed as input to the supervised machine learning model called back propagation neural network. A deep study conducted to train the net and analyzed in various views. The results were compared with the similar existing models which were using Random forest, Quicknat and deep learning. The proposed machine learning model produces the higher and similar dice scores of existing model. The validation of the proposed method yields 85% of dice score and 96% of sensitivity and 96% of specificity.

关键词： segmentation features back propagation algorithm hippocampus

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Application of artificial neural networks model on estimation of sea water levels along the northern Egyptian coast

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AEJ - Alexandria Engineering Journal 2004年第6期43卷 895-907页

作者： El-Ansary, A.E. Nagy, H.M. El-Mooty, M.M. Abd Ali, M.A. Hydraulics and Irrigation Eng. Dept. Faculty of Eng. Alexandria University Alexandria Egypt Coastal Research Institute National Water Research Center Alexandria Egypt

Most of different methods and models to predict sea water level require comprehensive exogenous inputs and involve some analysis along with certain assumptions. This paper describes the development of an Artificial Neural Networks (ANN) model to predict the sea water level along the Northern Egyptian Coast by using data of tidal gauges at different stations from Alexandria to El-Arish. A feed foreword back propagation algorithm was used to estimate the sea water level at stations along the coast. Before training could be carried out, the size of the network and the training parameters are assumed. In conclusion, the calculated daily sea water levels agree well with the measured values. The ANN's results gave better results compared with results obtained by other conventional technique based on statistical regression analysis. © Faculty of Engineering Alexandria University, Egypt.

关键词： Artificial neural networks model back propagation algorithm Linear regression analysis model Sea water level

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Intelligent control of the electrochemical nitrate removal basing on artificial neural network (ANN)

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JOURNAL OF WATER PROCESS ENGINEERING 2022年 49卷

作者： Meng, Guangyuan Fang, Liqiang Yin, Yao Zhang, Zhijie Li, Tong Chen, Peng Liu, Yongdi Zhang, Lehua East China Univ Sci & Technol Natl Engn Lab Ind Wastewater Treatment Shanghai 200237 Peoples R China Shanghai Municipal Engn Design Inst Grp Co Ltd Shanghai 200092 Peoples R China Bozhou Univ Continuing Educ Ctr Bozhou 236800 Peoples R China East China Univ Sci & Technol State Environm Protect Key Lab Environm Risk Asses Shanghai 200237 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai 200092 Peoples R China 130 Meilong Rd Shanghai 200237 Peoples R China

Artificial intelligence technologies were confirmed as a useful tool for wastewater treatment, but its application in the electrochemical nitrate removal had less been reported. In this work, the artificial neural network in machine learning was used to construct the model, which combines the methods of electrochemistry and arti-ficial intelligence to achieve the prediction and intelligent control of nitrate removal. The control system consists of a prediction module with an artificial neural network (ANN) algorithm model and a control module. First, initial nitrate concentration, pH, time and current density were considered as input. An ANN algorithm using 7 hidden layers and a negative feedback regulation mechanism was developed to optimize the model and predict the nitrate removal rate. Results indicates that the proposed prediction model (4-7-1) yields a better coefficient of determination and lower root mean square error. The optimal set-points of the current density in the elec-trochemical process can be obtained according to the water quality change and qualified effluent quality using the ANN model. Also, the proposed intelligent control strategy can eliminate the influence of water quality change on nitrate removal and reduce energy consumption by 15.0 % compared to the post strategy. This work demonstrated the potential of artificial intelligence in the electrochemical process of nitrate removal.

关键词： Electrochemistry Nitrate removal Artificial intelligence back propagation algorithm Artificial neural network

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Research on the BP-network-based Iris Recognition

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2015年第3期2卷 162-165页

作者： Dai, Fengzhi Fan, Li Yu, Chunyu Liu, Bo Bai, Ruixiang Tianjin Univ Sci & Technol Coll Elect Informat & Automat Tianjin 300222 Peoples R China

Iris recognition is the highly trusted identification recognition technology among methods of biological recognition. In this paper, we use the back propagation algorithm to train the neural network, so as to establish the iris recognition system model. The experiment demonstrates that it has a high recognition rate and the recognition speed is reasonable. The proposed method provides a convenient way for iris recognition.

关键词： iris recognition artificial neural network back propagation algorithm pattern recognition

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City Fire Risk Assessment Model Based on the Adaptive Genetic algorithm and BP Network

City Fire Risk Assessment Model Based on the Adaptive Geneti...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Jiao Aihong Yuan Lizhe Chinese Peoples Armed Police Forces Acad Dept Fire Commanding Langfang 065000 Peoples R China Nanjing Artillery Acad No 3 Dept Langfang 065000 Peoples R China

ISBN: (纸本)9780769547923

Based on the risk evaluation index system of city fire, a comprehensive evaluation model with the adaptive genetic algorithm and BP neural network (AGA-BP) is established in the article. In former process of the hybrid algorithm, the adaptive genetic algorithm is applied to adjust weights and thresholds of the three-layer BP neural network and train the BP neural network for locating the global optimum, and the error back propagation algorithm is used to search in neighborhoods of the approximate optimal solution in the later process. The program written in VB6.0 is used to learn some samples of city fire risk according to the AGA-BP algorithm and the general BP algorithm. The results show that the learning precision of AGA-BP algorithm is more correctly than that of the general BP algorithm. The training speed and convergence rate of the former is significantly improved because of the combination of AGA and BP algorithm. It is helpful to realize automated evaluation for city fire risk.

关键词： fire risk assessment adaptive genetic algorithm back propagation algorithm

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The Application of Neural Network in Recognition of Fabric Weave Parameters

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Journal of Donghua University(English Edition) 2004年第3期21卷 94-97页

作者：张星烨高卫东徐伯俊 School of Textile & Garment Southern Yangtze University Wuxi 214036 China

This paper describes a new method to identify the type of fabric weave by using a neural network classifier. The characteristic parameters of the input layer, derived from fabric image, are composed of the Markov random field character, the difference between the maximum and the minimum of gray level projections in weft and warp directions, the area ratio of the brightness region to the total area in image, the weft and the warp yarn count. The experimental results show that the neural network classifier can effectively classify fabric weave with 98.33% of accuracy, which is helpful in the recognition of fabric weave parameters.

关键词： neural network texture character back propagation algorithm

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Neural Network Identification of Underwater Vehicle with Hybrid Learning algorithm

Neural Network Identification of Underwater Vehicle with Hyb...

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31st Chinese Control Conference

作者： Wang Jian-guo China Ship Dev & Design Ctr Wuhan 430064 Peoples R China

ISBN: (纸本)9789881563811

A new dynamic neural network was constructed by borrowing ideas from Jordan and Elman neural networks. To accelerate the rate of convergence and avoid getting into local extremum, a hybrid learning algorithm by Genetic algorithm (GA) and error back propagation algorithm (BP) was used to tune the weight values of the network. Finally, the improved neural network was utilized to identify the AUV hydrodynamic model. The simulation results show that the new network can remember the history state of hidden layer and tune the effect of the past signal to the current value real-timely. And in the presented network, the feedback of output layer nodes is increased to enhance the ability of handling signals. The neural network by hybrid learning algorithm improves the learning rapidity of convergence and identification precision.

关键词： Underwater Vehicle System Identification Neural Network Genetic algorithm back propagation algorithm

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A Comparison of conventional and nonconventional methods of DC motor speed control

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IFAC Proceedings Volumes 2013年第8期46卷 50-53页

作者： Ermira Buzi Petraq Marango Electrical Engineering Faculty Polytechnic University of Tirana Tirana Albania Electrical Engineering Faculty Polytechnic University of Tirana

In this paper we study the speed control of a DC motor with conventional (PID) and nonconventional (Neural Network – NN) methods. DC motor model and performance indicators criteria are defined. PID controller is tuned through Matlab scripting and tools and relevant results of closed loops system are received. For nonconventional method inverse model control schema is used, where a three layer NN presents the inverse model, and it is trained by back propagation algorithm. Simulation results are presented to compare the two control methods and conclude the benefits of NN usage in control. Considering the broad range of DC motor usage, where robotics is included, the study is presented as a way for improving the closed system's dynamics.

关键词： Neural networks back propagation algorithm DC motor neural control PID controller

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Multilayer Neural Network and Its application to Adaptive Equilization

Multilayer Neural Network and Its application to Adaptive Eq...

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IJCNN International Joint Conference on Neural Networks

作者： Yongcheng Guo Baoyu Zheng Nanjing Institute of Communication Engineering Nanjing University of Posts&.Telecommunications

In the telephone channels,dispersion causes intetference betweensuccessive samples and greatly complicates reliable transmission and receptionof digital *** noise in telephone lines is generally low,and typically the maim problem is intersymbol *** combat this problem,much workhas been done since the mid-1960s on the adaptive equilization of telephonedata transmission *** the appearence of artificial neural networks,studies on models and applications of them are still in *** are recently being applied on various kinds of *** this paper a multilayer neural network with application to adaptive equilization is *** standstill in neural learning is avoided by an improved sigmoid function,and at the same time,the sample recognition rate is *** simunation gives a satisfied result.

关键词： Neural network back propagation algorithm adaptive equilization

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