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检索条件"主题词=Back propagation algorithm"

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Neural image recognition system with application to tuberculosis detection

Neural image recognition system with application to tubercul...

引用

International Conference on Information Technology - Coding and Computing

作者： Ramana, KV Khader, BS JNTUCE CSE Dept Kakinda AP India

ISBN: (纸本)0769521088

Now a days, image recognition systems have several applications in enormous fields. The use of recognition systems (based on Artificial Neural Networks) as means of predicting medical diagnosis and recommending successful treatments has been a highly active research field in past five years. The purpose of this paper is to construct and train an artificial neural network to serve as a knowledge base that can accurately detect Pulmonary Tuberculosis. The first necessary step is to preprocess the different patients MMR's, which consisted of lesions of Tuberculosis and extract the features. Then the extracted features are converted into usable format (gray scale values) and given to neural net for training. It is based on back propagation algorithm.. Then the knowledge base is used to detect a sample collected from a new patient is given as target to recognize it.

关键词： image processing artificial neural networks back propagation algorithm pulmonary tuberculosis

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ANN Based Adaptive Detection of ECG Features from Respiratory, Pleythsmographic and ABP Signals 3

ANN Based Adaptive Detection of ECG Features from Respirator...

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3rd International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Kumar, Vinay Laskar, Md. Azharuddin Singh, Yumnam Shantikumar Majumdar, Swanirbhar Sarkar, Subir Kumar NIT Meghalaya Dept ECE Shillong Meghalaya India Tezpur Univ Dept ECE Tezpur Assam India NIT Manipur Dept ECE Imphal Manipur India NERIST Dept ECE Itanagar Arunachal Prad India Jadavpur Univ Dept ETCE Kolkata W Bengal India

ISBN: (纸本)9783319119328

This paper presents the prediction of ECG features using artificial neural networks from respiratory, plethysmographic and arterial blood pressure(ABP) signals. One cardiac cycle of ECG signal consists of P-QRS-T wave. This process of feature prediction determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitude and interval values of ECG signal determine the functioning of heart for every human. This process is based on artificial neural network (ANN) and other signal analysis technique. In this process a feed forward multilayer perceptron network has been designed using back propagation algorithm. ECG signal is predicted from this network from the application of the respiratory, plethysmographic and ABP data to its input layer. For analyzing the data, a five point differentiation is done on the signal, so as to note the slope change of the resulting graph. Points with zero slopes were considered as the end of respective waves. The algorithm is tested with physionet database. The training and simulation results of the network have been obtained from Matlab7 (R) software.

关键词： Artificial Neural Network (ANN) Multilayer Perceptrons back propagation algorithm Five Point Differentiation ECG Respiratory Plethysmographic and Arterial Blood Pressure(ABP)

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Neural Network Model for Designing Monopole Antenna

Neural Network Model for Designing Monopole Antenna

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International Conference on Recent Advances in Microwave Theory and Applications

作者： Bose, Tanushree Gupta, Vibha Rani Gupta, Nisha Polytech Univ Ranchi 835215 Bihar India Birla Inst Technol Dept Elect & Commun Engn Ranchi 835215 Bihar India

ISBN: (纸本)9781424426904

In this paper, a neural network model is proposed which can be used for designing any monopole antenna whose element is square shaped and required to exhibit a wide bond characteristics. All the antennas designed using this model shows a wideband response between 4.5GHz-5 GHz. This model is prepared using Feed Forward back propagation algorithm. The results given by the prepared model are compared with the results of IE3D software which shows a very good agreement. Therefore, this model is accurate enough to yield the parameters of the monopole antenna thus eliminating the long time consuming process of determining various design parameters using costly software packages.

关键词： Monopole Antenna back propagation algorithm Neural Network

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Design and application of iris recognition system based on neural network

Design and application of iris recognition system based on n...

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International Conference of Information Science and Management Engineering (ISME)

作者： Yu, C. Dai, F. Zhang, J. Tianjin Univ Sci & Technol Sch Elect Automat Tianjin Peoples R China

ISBN: (纸本)9781845648299;9781845648282

Iris recognition is the high confidence personal identification technology among the other biometrics recognition. This is not only because of its unique feature but also due to its stability that the iris is immune to aging and environment. In this paper, we design a feedforward neural network and use the back propagation algorithm to build an elementary iris recognition system model. 10 iris samples are preprocessed in a simple methodology and are used as the recognition objects. Finally, the experiment demonstrates that though the recognition model is simply constructed, it has a high recognition rate and the recognition speed is reasonable. The proposed methodology provides a convenient way for iris recognition.

关键词： iris recognition artificial neural network back propagation algorithm image processing grayscale image

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Research on Battlefield Target Value Evaluation Model Based on Improved BP algorithm

Research on Battlefield Target Value Evaluation Model Based ...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Yuan Lizhe Rong Gaohua Gao Chunsheng Nanjing Artillery Acad Dept 3 Langfang 065000 Peoples R China

ISBN: (纸本)9780769547923

Target value evaluation is the core task in the battlefield information processing. How to evaluate the value of targets and calculate their priorities based on battlefield target information and the combat mission is a focuses problem in the modern war. To solve the problem, some comprehensive evaluation indexes for the targets are created and an evaluation model based on the improved BP algorithm for targets value is proposed in this paper. The result of simulation proves the validity of this algorithm. It's helpful to use this model in choosing the fire attack objects in practice.

关键词： Target value Evaluation model back propagation algorithm

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Implementation of Single Artificial Neuron Using various Activation Functions and XOR Gate on FPGA chip 2

Implementation of Single Artificial Neuron Using various Act...

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2nd IEEE International Conference on Advances in Computing and Communication Engineering (ICACCE)

作者： Abrol, Sahil Mahajan, Rita PEC Univ Technol Dept ECE Chandigarh India

ISBN: (纸本)9781479917341

In this paper, a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is done step by step. First single neurons are implemented using different activation function and then a XOR Gate is implemented. The concurrent structure of a neural network makes it very fast for the computation of certain tasks. This makes ANN well suited for implementation in VLSI technology. Hardware realization of a Neu- ral Network depends on the efficient implementation of Artificial single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. Here design and implementation of a Single neuron with various activation functions is done and then an XOR Gate is implemented using one of these designs. The design of XOR Gate is done by using gradient descent algorithm and taking 3-layers 5-neurons model.

关键词： Artificial neural network VHDL back propagation algorithm Xilinx FPGA Sigmoid Activation Function

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Neuro Fuzzy based controller for Power Quality Improvement 7

Neuro Fuzzy based controller for Power Quality Improvement

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Pedapenki, Kishore Kumar Gupta, S. P. Pathak, Mukesh Kumar IIT Roorkee Dept Elect Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9781509000760

Use of power electronic converters with nonlinear loads leads to power quality problems by producing harmonic currents and drawing reactive power. A shunt active power filter provides an elegant solution for reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varied, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with hybrid artificial intelligence controller, i.e. neuro fuzzy controller for shunt active power filter. The performance of neuro fuzzy controller over PI controller is examined and tabulated. The salvation of the problem is extensively verified with various loads and plotted the worst case out of them for the sustainability of the neuro fuzzy controller.

关键词： Active Power Filter Neuro Fuzzy Controller back propagation algorithm Soft Computing

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Granular neural networks models with class-belonging granulation

Granular neural networks models with class-belonging granula...

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proc 2014 inter conf contemporary comp informatics (ic3i)

作者： Kumar, D. Arun Meher, Saroj K. JNTU Inst Sci & Technol Sch Spatial Informat Technol Kakinada India Indian Stat Inst Syst Sci & Informat Unit Bangalore Karnataka India

ISBN: (纸本)9781479966295

Granular neural networks (GNNs) take the fuzzy granulated input and process them through neural networks (NNs). As a result, performance of GNNs depends highly on the granulation process and initial weights of NNs. The initial weights between nodes of GNNs provide the starting point in the searching of the lowest cost function value. The present article proposes GNN model that use class-belonging (CB) fuzzy granulation of input information and rough set-theoretic weight initialization of NNs. The model thus avoids the random initialization of weights and provides improved decisions at the output with CB granulation. Classification performance of the proposed GNN model has been assessed using various measurement indexes and its superiority over similar other methods is justified. Conventional back propagation algorithm is used to train the proposed model of GNN.

关键词： backpropagation fuzzy set theory neural nets pattern classification rough set theory CB fuzzy granulation GNN back propagation algorithm class-belonging granulation classification performance cost function value fuzzy granulated input granular neural network model measurement index rough set-theoretic weight initialization Accuracy Artificial neural networks Computational modeling Data models Informatics Training

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A Performance Evaluation of Probabilistic Vs. Deterministic Spiking Neural Network 22

A Performance Evaluation of Probabilistic Vs. Deterministic ...

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Sedghi, Maryam Ahmadi, Arash Eskandari, Elahe Heydari, Ramiyar Razi Univ Dept Elect Engn Kermanshah Iran

ISBN: (纸本)9781479944095

this paper aims to present a comparison between probabilistic and deterministic spiking neural network for a back propagation classification algorithm. To have a fair comparison, neuron models and structures are considered identical in both of the networks. The networks are trained and tested with the Iris database. According to the simulation results, the probabilistic network converges faster than the deterministic one, where it is also more sensitive to the input variations. The simulation results show a precision of 90% 88% for the probabilistic and the deterministic networks correspondingly, which is in consistence with the similar results for linear neural networks.

关键词： Probabilistic Spiking Neural Network (PSNN) Spiking Neural Network (SNN) back propagation algorithm

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An Artificial Intelligence Approach for Predicting Different Types of Stroke 2

An Artificial Intelligence Approach for Predicting Different...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Haridas, Hima Wilson, Aswathy Jyothi Engn Coll Dept Comp Sci Trichur India

ISBN: (纸本)9781538619742

Stroke is a second leading cause of death and they have been serious, long-term incapacity. Stroke is the sudden demise of cerebrum cells because of a nonattendance of oxygen, caused by blockage of bloodstream or break of a supply route to the mind. According to World Health Organization in the upcoming year's stroke will continue growth death rate. The many works have been carried out for detecting stroke *** artificial intelligence approach for predicting stroke and its types using deep learning. Types are Ischemic stroke, Hemorrhagic Stroke, Transient Ischemic *** our work, dataset collection from the medical institute. The preprocessing method expels copy records, missing information, and conflicting information. Principle component analysis algorithm is computation is used for reducing the estimations and deep learning utilizing predicts whether the patient is encountering stroke illness or not. So as to foresee the stroke illness, it actualizes classification by deep learning. When the patient details are entered, it checks with trained model and forecasting of different types of *** this work mainly focuses better way for predicting stroke and different type of stroke.

关键词： artificial intelligence artificial neural network back propagation algorithm

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