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Testing Artificial Metaplasticity in MLP Applications

Testing Artificial Metaplasticity in MLP Applications

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Andina, Diego Marcano-Cedeno, Alexis Torres, Joaquin Alarcon, Martin J. Tech Univ Madrid UPM Grp Automat Signals & Commun Madrid Spain

ISBN: (纸本)9781424427932

In this work we tested and compared Artificial Metaplasticity (AMP) results for Multilayer Perceptrons (MLPs). AMP is a novel Artificial Neural Network (ANN) training algorithm inspired on the biological metaplasticity property of neurons and Shannon's information theory. During training phase, AMP training algorithm gives more relevance to less frequent patterns and subtracts relevance to the frequent ones, claiming to achieve a much more efficient training, while at least maintaining the MLP performance. AMP is specially recommended when few patterns are available to train the network. We implement an Artificial Metaplasticity MLP (AMMLP) on standard and well-used databases for Machine Learning. Experimental results show the superiority of AMMLPs when compared with recent results on the same databases.

关键词： Neural Network MLPs backpropagation algorithm Pattern Recognition Metaplasticity

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Multilayer neural networks in handwritten recognition

Multilayer neural networks in handwritten recognition

引用

International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET 2004)

作者： Ciota, Z Gawinowski, M Tech Univ Lodz Dept Microelect & Comp Sci PL-90924 Lodz Poland

ISBN: (纸本)9665533800

Multilayer neural network presented in this paper can be helpful for recognition of handwritten words. Learning efficiency of such network has been also presented. For high learning efficiency a standard backpropagation method has been extended, by adding a momentum term. Additional analysis shows how to improve proposed solutions using mixed hardware-software co-design and fuzzy logic approach.

关键词： neural networks handwritten recognition optical character recognition backpropagation algorithm

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REAL-TIME FAULT-DIAGNOSIS - USING OCCUPANCY GRIDS AND NEURAL NETWORK TECHNIQUES 5th

引用

5TH INTERNATIONAL CONF ON INDUSTRIAL AND ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS ( IEA/AIE-92 )

作者： RAY, AK MISRA, RB Reliability Engineering Centre Indian Institute of Technology Kharagpur 721 302 WB India

ISBN: (纸本)354055601X

This paper presents a methodology for real-time fault diagnosis of manufacturing systems using occupancy grids and neural network techniques. The main advantages of the system over other existing methods are its ability to capture imprecise and time dependent information, ability to accommodate nonlinear relationships, ability to learn and acquire knowledge automatically. A case study related to real-time milling machine fault diagnosis is discussed. The paper also discusses the problems with the proposed method and the future research directions. © 1992, Springer Verlag. All rights reserved.

关键词： NEURAL NETWORKS OCCUPANCY GRIDS INFORMATION MEASURE backpropagation algorithm

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Comparison of Sigmoidal FFANN Training algorithms for Function Approximation Problems 2

Comparison of Sigmoidal FFANN Training Algorithms for Functi...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Bhatia, M. P. S. Veenu Chandra, Pravin NSIT Div Comp Engn New Delhi India Guru Gobind Singh Indraprastha Univ GGSIPU USICT New Delhi India

ISBN: (纸本)9789380544168

The estimation of unknown function from a number of data inputs has number of various applications like in Engineering, Artificial intelligence, Statistics, Artificial Neural Networks, Genetic algorithms etc. Many papers have described the individual methods. But very less is known about the comparative performance of various methods. In this paper we give the comparative performance of the neural network using ten different approximation functions and twelve various training algorithms. Our study uses MATLAB 2013a 8.1 Neural Network toolbox for experimentation. The performance of the method on the neural network depends on the approximation function type and the various properties of training data. We found that Bayesian Regulation backpropagation method proved to be best in performance using function 6 given in the paper out of twelve different algorithms used.

关键词： Artificial Neural Network (ANN) backpropagation algorithm Feed Forward Artificial Neural Network (FFANN) Function approximation

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A wavelet-based neural network applied to surface defect detection of LED chips

A wavelet-based neural network applied to surface defect det...

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4th International Symposium on Neural Networks (ISNN 2007)

作者： Lin, Hong-Dar Chung, Chung-Yu Chaoyang Univ Technol Dept Ind Engn & Management 168 Jifong E Rd Taichung 41349 Taiwan

ISBN: (纸本)9783540723929

This research explores the automated detection of surface defects that fall across two different background textures in a light-emitting diode (LED) chip. Water-drop defects, commonly found on chip surface, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop defect is difficult because the defect has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. We first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. Then, the Multi-Layer Perceptron (MLP) neural network with back-propagation (BPN) algorithm is applied to integrate the multiple wavelet characteristics. Finally, the wavelet-based neural network approach judges the existence of water-drop defects. Experimental results show that the proposed method achieves an above 96.8% detection rate and a below 4.8% false alarm rate.

关键词： computer vision system LED chip surface defect detection wavelet decomposition multi-layer perceptron neural network with backpropagation algorithm

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Neural Network Approach to the Prediction of Percentage Data Packet Loss for Wireless Sensor Networks

Neural Network Approach to the Prediction of Percentage Data...

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41st Southeastern Symposium on System Theory

作者： Barve, Yogesh D. Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA

ISBN: (纸本)9781424433247

Wireless sensor networks are used in the field of communications and have gained enormous popularity in recent times. Depending upon the environment in which the wireless sensor network operates, the amount of noise level would differ and hence the data packet loss in wireless communication would vary. This paper presents a solution to the prediction of percentage data packet loss in the wireless sensor network in indoor and outdoor environment. It uses the Artificial Neural Network (ANN) to predict the data packet loss and the Erasure Coding technique to find the actual percentage data packet lost in wireless sensor network. The results obtained from the ANN are compared to the respective ones yielded by the Erasure Coding technique and are found to exhibit satisfactory accuracy.

关键词： backpropagation algorithm Neural network Wireless sensor network

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A hierarchical neuro-fuzzy system based on S-implications

A hierarchical neuro-fuzzy system based on S-implications

引用

International Joint Conference on Neural Networks

作者： Nowicki, R Scherer, R Rutkowski, L Czestochowa Tech Univ Dept Comp Engn PL-42200 Czestochowa Poland

In this paper we present a neuro-fuzzy structure of the Hierarchical Prioritized Structure (HPS) proposed by Yager. The HPS allows for easy hierarchization of a fuzzy rule-base. Our neuro-fuzzy system can be learned b... 详细信息

ISBN: (纸本)0780378989

关键词： fuzzy neural nets hierarchical systems backpropagation hierarchical neuro-fuzzy system S-implications hierarchical prioritized structure fuzzy rule-base backpropagation algorithm computationally efficient algorithm backpropagation algorithms Fuzzy neural networks Hierarchical systems back propagation fuzzy rule base Fuzzy systems efficient algorithm Hierarchical

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Robust Multiclass ECG Arrhythmia Detection Using Balanced Trained Neural Network

Robust Multiclass ECG Arrhythmia Detection Using Balanced Tr...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Dash, Sanjit K. Rao, G. Sasibhushana Raajdhani Engn Coll Dept ECE Bhubaneswar Orissa India Andhra Univ Coll Engn Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781467399395

Classification of data including Electrocardiogram (ECG) is significantly affected with imbalanced class distribution. Efforts have been made to learn from the imbalanced data to improve the performance of the classifier. This paper presents the result of ECG arrhythmia detection of normal(N) beat and five different arrhythmia beats based on balanced input to the classifier for training only. The ECG signals are taken from MIT-BIH arrhythmia database. A three layer feed-forward backpropagation neural network(FFBNN) is used for classification. The input for training the network is perfectly balanced, taking same number of patterns from each class. After training the trained network is used for classifying completely different dataset. We achieved average accuracy, sensitivity, & specificity of 99.24%, 94.90% & 99.57% respectively. The effect of balanced input at the time of training is shown.

关键词： Balanced trained Artificial Neural Network ECG Arrhythmia Muticlass Pattern Classification backpropagation algorithm

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Matrix-Valued Neural Networks 21st

Matrix-Valued Neural Networks

引用

21st International Conference on Soft Computing (MENDEL)

作者： Popa, Calin-Adrian Polytech Univ Timisoara Dept Comp & Software Engn Timisoara 300223 Romania

ISBN: (纸本)9783319198248;9783319198231

This paper introduces matrix-valued feedforward neural networks, for which the inputs, outputs, weights and biases are all square matrices. This type of networks represents a natural generalization of the complex-, hyperbolic-, quaternion- and Clifford-valued neural networks that have been intensively studied over the last few years. The full deduction of the gradient descent algorithm for training such networks is presented. The proposed networks are tested on three synthetic function approximation problems, with promising results for the future.

关键词： Clifford-valued neural networks backpropagation algorithm Matrix-valued neural networks

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Classification of pulmonary diseases based on impulse oscillometric measurements of lung function using neural networks

Classification of pulmonary diseases based on impulse oscill...

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26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Barúa, M Nazeran, H Nava, P Granda, V Diong, B Univ Texas Dept Elect & Comp Engn El Paso TX 79968 USA

ISBN: (纸本)0780384393

Central and peripheral airflow obstructions frequently occur in patients with chronic obstructive lung disease or asthma and may have different pathophysiological mechanisms of obstruction and require different therapeutic interventions. Impulse oscillometry (IOS) is a patient-friendly method for studying respiratory function in health and disease. The enormous variety of patterns and the high degree of variability in the measured lung function parameters has made the automated diagnosis of pulmonary diseases very desirable by pulmonary physiologists and clinicians. Computer aided diagnosis can serve as a second but quantitative opinion to diagnosis and screening. Presently, there are no robust algorithms to classify the IOS patterns into particular disease groups. In this work, an artificial neural network (ANN) was used to recognize and classify the diseases of the central and peripheral airways. Using IOS measurements in 131 patients, a training set was created and used in a feedforward ANN that was trained by backpropagation algorithm to classify pulmonary diseases as either central or peripheral. After supervised training, the ANN was presented with the same data and produced a 98.47% correct classification rate. When a new set of unseen data was used, the classification accuracy was reduced to 61.53%. Having produced a promising classification rate in the first case, the accuracy of this classifier could be further improved. Inclusion of more training samples combined with fuzzy logic decision rules could facilitate the development of a software tool that assists pulmonary specialists with their diagnosis of lung function using the patient-friendly IOS system.

关键词： impulse oscillometry (IOS) artificial neural networks backpropagation algorithm pulmonary disease classification

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