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15th International Conference on Advanced Communications Technology (ICACT)

作者： Payal, Ashish Rai, C. S. Reddy, B. V. R. GGS Indraprastha Univ Univ Sch Informat & Commun Technol New Delhi India

ISBN: (纸本)9788996865018;9781467331487

Wireless sensor networks (WSNs) have many applications in the field of disaster management, military, healthcare and environmental monitoring. Capability of WSNs is further enhanced by the efficient localization algorithms. Localization is the process by which a sensor node determines its own location after deployment. Neural approaches are gaining popularity in evolving new localization algorithms that are capable of optimizing various parameters of WSNs. In this paper, we analyse two backpropagation algorithms based on multi-layer Perceptron (MLP) neural network. The network is trained using static sensor nodes placed in a grid with their coordinates known. The input values are distances from each anchor nodes to a particular sensor node. The output is the actual coordinates of the sensor nodes. After training, the network will be able to predict the coordinates of unknown sensor nodes. This MLP model is analyzed for Bayesian regularization and Levenberg-Marquardt training algorithm. Both algorithms are tested for the robustness and cross-validation. The simulation results demonstrate the effectiveness of the proposed model on localization error.

关键词： Localization Wireless Sensor Network backpropagation algorithm Neural network Bayesian regularization

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backpropagation and Levenberg-Marquardt algorithm for Training Finite Element Neural Network

Backpropagation and Levenberg-Marquardt Algorithm for Traini...

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6th UKSim/AMSS European Symposium on Computer Modeling and Simulation (EMS)

作者： Reynaldi, Arnold Lukas, Samuel Margaretha, Helena Pelita Harapan Univ Fac Sci & Math Tangerang Banten Indonesia Pelita Harapan Univ Fac Comp Sci Tangerang Banten Indonesia

ISBN: (纸本)9780769549262

In this paper, finite element based neural network is developed. The purpose is to solve differential equation and inverse problem of differential equation. Inverse problem of differential equation is a problem to solve for parameters of differential equation, assuming that the solution of the differential equation is already known beforehand. Inverse problem mainly used to approximate physical parameters of material. Finite element method will be combined with artificial neural network using backpropagation algorithm to solve differential equation and Levenberg-Marquardt training algorithm to solve inverse differential problem. By using proposed method, invers matrix calculation will not be needed for solving both differential equation and inverse differential problem. From any given differential equation, the solution will be solved first. And the solution is used to validate the parameter in differential equation, namely to solve inverse problem of that differential equation.

关键词： finite element method artificial neural network backpropagation algorithm Levenberg-Marquardt algorithm inverse differential problem

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Optimization of type-2 Fuzzy Weight for Neural Network using Genetic algorithm and Particle Swarm Optimization

Optimization of type-2 Fuzzy Weight for Neural Network using...

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World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479914159

In this paper two bio-inspired methods are applied to optimize the type-2 fuzzy inference systems used in the neural network with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the learning method architecture and the adaptation of type-2 fuzzy weights are presented. In this work an optimized type-2 fuzzy inference systems to manage weights for the neural network and the results for the two bio-inspired methods are presented. The proposed approach is applied to a case of time series prediction, specifically in Mackey-Glass time series.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

Backpropagation Learning Method with Interval Type-2 Fuzzy W...

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International Joint Conference on Neural Networks

作者： Fernando Gaxiola Patricia Melin Fevrier Valdez Oscar Castillo Tijuana Institute of Technology Tijuana México

ISBN: (纸本)9781467361279

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system Neural network backpropagation algorithms Learning methods Fuzzy back propagation

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Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique

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AASRI Procedia 2013年 4卷 306-312页

作者： Amit Choudhary Rahul Rishi Savita Ahlawat Maharaja Surajmal Institute New Delhi India UIET Maharshi Dayanand University Rohtak India Maharaja Surajmal Institute of Technology New Delhi India

The choice of pattern classifier and the technique used to extract the features are the main factors to judge the recognition accuracy and the capability of an Optical Character Recognition (OCR) system. The main focus of this work is to extract features obtained by binarization technique for recognition of handwritten characters of English language. The recognition of handwritten character images have been done by using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

关键词： OCR Binarization Feature Extraction Character Recognition backpropagation algorithm Neural Network

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Off-Line Handwritten Character Recognition using Features Extracted from Binarization Technique

Off-Line Handwritten Character Recognition using Features Ex...

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2013 AASRI Conference on Intelligent Systems and Control（ISC 2013）

作者： Amit Choudhary Rahul Rishi Savita Ahlawat Maharaja Surajmal Institute UIET Maharshi Dayanand University Maharaja Surajmal Institute of Technology

The choice of pattern classifier and the technique used to extract the features are the main factors to judge the recognition accuracy and the capability of an Optical Character Recognition（OCR） system. The main focus of this work is to extract features obtained by binarization technique for recognition of handwritten characters of English language. The recognition of handwritten character images have been done by using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

关键词： OCR Binarization Feature Extraction Character Recognition backpropagation algorithm Neural Network

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Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

Electroencephalography Analysis Using Neural Network and Sup...

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2013 International Conference on Biomedical Engineering(ICBE 2013)

作者： JeeEun Lee Sun K.Yoo Graduate School of Biomedical Engineering Yonsei University Department of Medical Engineering College of Medicine Yonsei University

The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography（EEG） signal. To compare and evaluate system performance, the rules of Recht-schaffen and Kales（R＆K rule） were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine （SVM）. As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.

关键词： Sleep Electroencephalography Neural Network backpropagation algorithm SVM

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Design of an Under-Actuated Exoskeleton System for Walking Assist While Load Carrying

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ADVANCED ROBOTICS 2012年第5-6期26卷 561-580页

作者： Yu, S. N. Lee, H. D. Lee, S. H. Kim, W. S. Han, J. S. Han, C. S. Hanyang Univ Ansan 426791 Kyeonggi Do South Korea Korea Atom Energy Res Inst Taejon 305353 South Korea Hansung Univ Seoul 136792 South Korea

This study proposes an under-actuated wearable exoskeleton system to carry a heavy load. To synchronize that system with a user, a feasible modular-type wearable system and its corresponding sensor systems are proposed. The design process of the modular-type exoskeleton for lower extremities is presented based on the considered requirements. To operate the system with the user, human walking analysis and intention signal acquisition methods for actuating the proposed system are developed. In particular, a sensing data estimation strategy is applied to synchronize the exoskeleton system with a user correctly. Finally, several experiments were performed to evaluate the performance of the proposed exoskeleton system by measuring the electromyography signal of the wearer's muscles while walking on level ground and climbing up stairs with 20- to 40-kg loads, respectively. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012

关键词： Wearable robot exoskeleton constant force mechanism muscle stiffness sensor backpropagation algorithm

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ANN based Sensor Faults Detection, Isolation, and Reading Estimates - SFDIRE: Applied in a nuclear process

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ANNALS OF NUCLEAR ENERGY 2012年 49卷 131-142页

作者： Elnokity, Omar Mahmoud, Imbaby I. Refai, Mohamed K. Farahat, Hasan M. Al Azhar Univ Fac Engn Cairo Egypt Atom Energy Author NRC Cairo Egypt

In this paper a new method is developed based on the capabilities of the Artificial Neural Networks (ANNs) to detect, isolate, and output an estimated reading of faulty sensor/s reading/s that serves in monitoring a complex process. For a number of measured signals working to monitor a process, we allow training of an equal number of ANN's to those signals. Each ANN has an output signal equal to one corresponding measured signal and the set of inputs is the rest of other measured signals and other networks output signals using two-hidden layer feedforward network with different number of neurons for each. After the measuring the error between the real signal and the ANN output, the faulty sensor can be detected. The reading of this faulty sensor could be isolated and an estimated reading is sent to the monitoring system. This method could be applied for any difficult to model process to increase its availability and in the same time maintaining safety. In this paper, the application considered is the thermal-hydraulics process of Egyptian Training and Research Reactor-2 (ETRR-2) core cooling system. Data from the reactor are used to train the networks using backpropagation algorithm. An approximated model based on the lumped parameters approach for the thermal-hydraulics of the reactor core is built to compare results against ANN based system. The proposed system deals with 6 signals. From comparison, very good results are achieved when testing the design. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ANN Artificial Neural Networks SFDIRE Feedforward Hidden layer backpropagation algorithm ETRR-2

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Twisted Quaternary Neural Networks

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第4期7卷 397-401页

作者： Kobayashi, Masaki Nakajima, Akihiro Univ Yamanashi Interdisciplinary Grad Sch Med & Engn Kofu Yamanashi 4008511 Japan

The quaternary neural network (QNN) proposed by Nitta is a high-dimensional neural network. Nitta showed that its learning is faster than that of ordinary neural networks and the number of required parameters is almost one-third of that of real-valued neural networks by computer simulations. In this paper, we propose the twisted quaternary neural network (TQNN) which modifies the directions of multiplications of the QNN. Since quaternions are noncommutative on multiplication, we can get another neural network. When the activation function is linear, multilayered neural networks can be expressed by single-layered neural networks. But the TQNN cannot be expressed by a single-layered QNN even if the activation function is linear. Therefore, the TQNN is expected to produce a variety of signal-processing systems. We performed computer simulations to compare the QNN and the TQNN. Then we found that the latter's learning is a little faster. Moreover, computer simulation showed that the QNN tended to be trapped in local minima or plateaus but the TQNN did not. It is said that reducibility causes local minima and plateaus. We discuss the differences of reducibility between the QNN and the TQNN as well. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： neural networks quaternion learning backpropagation algorithm

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