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Neurofuzzy Inference System for Diagnosis of Malaria

Neurofuzzy Inference System for Diagnosis of Malaria

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International Conference on Innovative Applications of Computational Intelligence on Power Energy and Controls with their Impact on Humanity (CIPECH)

作者： Rastogi, Aayush Gupta, Neeraj Kumar Tyagi, Praveen Kumar KIET Dept Elect & Elect Engn Ghaziabad India

ISBN: (纸本)9781479958726

In this paper, a structure of adaptive system is proposed with the help of Neurofuzzy System (NFS) for diagnosis of Malaria. Investigation of malaria using Neurofuzzy system has been used for decision making ability based on predefined rules and learning by the backpropagation algorithm. Mapping Network in backpropagation algorithm is applied to minimize the errors in the output. Investigation of malaria by the proposed system is illustrated and good performance is achieved with maximum instant error of 0.06144.

关键词： Neurofuzzy inference system backpropagation algorithm Malaria

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Guide Data Generation for On-Line Learning of DBM-Initialized MLP

Guide Data Generation for On-Line Learning of DBM-Initialize...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781509018970

In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational cost in terms of time, and the learning process may not converge to good result. To solve this problem, we propose to use k-means to cluster all candidates of guide data, and use one datum from each cluster as the guide datum. We conducted experiments on several public databases, using different settings, and confirmed the performance of the proposed method. Specifically, if k=5, we can obtain good models with low computational cost through on-line learning.

关键词： Decision Boundary Making Multilayer Perceptron backpropagation algorithm On-Line Learning

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Drift3Flow: Freeway-Incident Prediction using Real-Time Learning 18

Drift3Flow: Freeway-Incident Prediction using Real-Time Lear...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Moreira-Matias, Luis Alesiani, Francesco NEC Labs Europe Kurfursten Anlage 36 D-69115 Heidelberg Germany

ISBN: (纸本)9781467365963

Traffic congestion is a major problem on today's urban mobility. This paper introduces a novel model for Automatic Incident Prediction (AID) on freeways: Drift3Flow. This stepwise methodology produces flow/occupancy rate predictions using an online weighted ensemble schema of two well-known time series analysis techniques: Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (ETS). Then, it continuously monitors the probability distribution function (p.d.f.) of the prediction residuals to trigger alarms of an imminent prediction divergence, i.e. concept drift. Such alarm activates an update neuron which extends our model's reactivity by embedding a fully incremental learning schema inspired on the Delta Rule (DR) (derived from the backpropagation (BP) algorithm). Our experimental test-bed used three weeks of data acquired from a real-world sensor network in Asia. The results validated its contributions by exhibiting a superior performance: 25% greater than the one obtained using ARIMA and ETS-based AID methods.

关键词： autoregressive moving average processes backpropagation learning (artificial intelligence) road traffic time series traffic information systems ARIMA Asia BP DR Drift3Flow ETS-based AID methods Holt-Winters exponential smoothing automatic incident prediction autoregressive integrated moving average backpropagation algorithm delta rule flow-occupancy rate predictions freeway-incident prediction online weighted ensemble schema prediction divergence probability distribution function real-time learning time series analysis techniques traffic congestion update neuron urban mobility Event detection Neurons Prediction algorithms Predictive models Real-time systems Time series analysis Traffic control

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A novel learning algorithm for feedforward networks using lyapunov function approach

A novel learning algorithm for feedforward networks using ly...

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International Conference on Intelligent Sensing and Information Processing

作者： Behera, L Kumar, S Patnaik, A Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)0780382439

This paper investigates a new learning algorithm (LF I) based on Lyapunov function for the training of feedforward neural networks. The proposed algorithm has an interesting parallel with the popular back-propagation algorithm where the fixed learning rate of the back-propgation algorithm is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. Next, the proposed algorithm is modified (LF II) to allow smooth search in the weight space. The performance of the proposed algorithms is compared with back-propagation algorithm and extended Kalman filtering(EKF) on two bench-mark function approximations, XOR and 3-bit Parity. The comparisons are made in terms of learning iterations and computational time required for convergence. It is found that the proposed alogorithms (LF I and II) are faster in convergence than other two algorithms to attain same accuracy. Finally the comparison is made on a system identification problem where it is shown that the proposed algorithms can achieve better function approximation accuracy.

关键词： feedforward neural nets Lyapunov methods backpropagation Kalman filters filtering theory function approximation convergence of numerical methods feedforward neural networks training Lyapunov function backpropagation algorithm adaptive learning rate convergence theorem Lyapunov stability theory EKF extended Kalman filtering theory benchmark function approximation XOR function system identification

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SOFT COMPUTING BASED FAULT DETECTION & CLASSIFICATION FOR TRANSMISSION LINES

SOFT COMPUTING BASED FAULT DETECTION & CLASSIFICATION FOR TR...

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

作者： Jain, Ankit Moses, Beaulah Vikrant Inst Technol & Management Dept Elect Engn Gwalior India

ISBN: (纸本)9781467399395

This Paper focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

关键词： Artificial Neural Networks Feedforward networks backpropagation algorithm Levenberg-Marquardt algorithm

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Parallel Implementation of Genetic algorithm for Searching Optimal Parameters of Artificial Neural Networks 3

Parallel Implementation of Genetic Algorithm for Searching O...

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3rd International Conference on Science and Technology - Computer (ICST)

作者： Nugroho, Eko Dwi Wibowo, Moh Edi Pulungan, Reza Univ Gadjah Mada Fac Math & Nat Sci Dept Comp Sci & Elect Yogyakarta Indonesia

ISBN: (纸本)9781538618745

Genetic algorithm is a population-based evolutionary optimization approach, and this has been used to train artificial neural networks successfully. When a large number of individuals make up the population, however, the running time of the algorithm often becomes very long. Parallel computation is a technique that can potentially be used to address this issue. This research investigates the adoption of parallel genetic algorithm for searching optimal parameters of artificial neural networks (multilayer perceptrons). Paralellization is conducted using Message Passing Interface, where sub-populations (and their fitness values) are exchanged between processors while selection, crossover, and mutation processes, which are necessary to generate new sub-populations, are performed independently between processors. Experiments of this research show that parallelization has indeed reduced the running time of genetic algorithm. The proposed method produces an average running time of 2.066, 2.656, and 47.788 seconds/generation for the game of Tic-Tac-Toe, car evaluation, and game of chess (King-Rook vs. King-Pawn) datasets, respectively. For comparison, the average running time of the serial version of the algorithm is 14.397, 12.961, and 350.963 seconds/generation for the same three datasets, respectively.

关键词： parallel computation genetic algorithm artificial neural networks backpropagation algorithm message-passing interface

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An Object Linked Intelligent Classification Method for Hyperspectral Images 37

An Object Linked Intelligent Classification Method for Hyper...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Mishra, Ashutosh Rajput, N. S. Singh, K. P. Singh, Dharmendra BHU Indian Inst Technol Dept Elect Engn Varanasi Uttar Pradesh India Indian Inst Technol Dept Elect & Commun Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9781509049516

Hyperspectral images have recently become one of the finest basis for highly accurate identification of objects. Such images, however, are very large in size and carry huge information. Processing and handling of such information is also quite resource savvy and require complex algorithms as well. In this paper, an intelligent classification method has been proposed. Using two independent, simple feed-forward artificial neural networks (ANN), a 426 band hyperspectral image has been first processed for dimensionality reduction to 15 principal components (PCs) using standardized principal component analysis (SPCA), containing 98.86% of the original information. In the second stage, these 15 PCs have been utilized to train another ANN and three different objects viz. road, soil and vegetation have been accurately identified using supervised learning. Only a portion of hyperspectral image data was used as training set and three unique signature patterns were created in the form of pattern library for road, soil and vegetation. Once trained, three independent hyperspectral images taken from San Joachim field site located in California (NEON Domain 17) were fed into the well trained two-stage ANN. The three aforesaid objects were correctly identified. The proposed method is fully scalable and a pattern library can be created to identify more classes of objects. Once trained, the proposed method does not require any more statistical process and all subsequent images can be processed on-board due to the method's implementation using ANNs.

关键词： Artificial Neural Network backpropagation algorithm Classification Hyperspectral imagery Standardized principal component analysis Remote sensing

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Additive Non-Gaussian Noise Channel Estimation by Using Minimum Error Entropy Criterion 7

Additive Non-Gaussian Noise Channel Estimation by Using Mini...

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7th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Heravi, Ahmad Reza Hodtani, Ghosheh Abed Ferdowsi Univ Mashhad Elect Engn Dept Mashhad Iran

ISBN: (纸本)9781538608043

channel estimation is an important component of wireless communications. This paper deals with the comparison between Mean Square Error (MSE) based neural networks and Minimum Error Entropy (MEE) based neural networks in additive non-Gaussian noise channel estimation. This essay analyzes MEE and MSE algorithms in several channel models utilizing neural networks. The aim of this study is first to compare the performance of an MSE-based conjugate gradient backpropagation (BP) algorithm with MEE-based BP method. The trained neural networks can be applied as an equalizer in the receiver. Moreover, to make a complete comparison between methods, we compare them in both low and high SNR regimes. The numerical results illustrate that MEE-based back propagation algorithm is more capable than the MSE-based algorithm for channel estimation. In fact, with additive non-Gaussian noise the performance of the MSE can be approximately as same as the MEE results in high SNR regime, but the MEE outperforms MSE-based method obviously in low SNR regime with non-Gaussian noise.

关键词： Entropy Mean square error Neural network Information theoretic learning Channel estimation backpropagation algorithm

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Using Particle Swarm Optimization in Training Neural Network for Indoor Field Strength Prediction

Using Particle Swarm Optimization in Training Neural Network...

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51st International Symposium ELMAR

作者： Vilovic, Ivan Burum, Niksa Milic, Dorde Univ Dubrovnik Dubrovnik Croatia

ISBN: (纸本)9789537044107

This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training a neural network, so we compared BP training algorithms: gradient descent method and Levenberg- Marquardt algorithm with PSO algorithm. PSO algorithm has been shown as powerful method for global optimization in several applications. A floor of university building in Dubrovnik has been used as case for simulation and measurement of signal strength. The results show that the neural network weights converge faster with PSO than with standard BP algorithms.

关键词： Neural network Multilayer Perceptron Training algorithm backpropagation algorithm Gradient descent algorithm Levenberg-Marquardt algorithm Particle swarm optimization

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Biometric retina identification based on neural network 12th

Biometric retina identification based on neural network

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12th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Sadikoglu, Fahreddin Uzelaltinbulat, Selin Near East Univ Dept Elect & Elect Engn POB 99138 TR-10 North Cyprus Mersin Turkey Near East Univ Dept Comp Engn POB 99138 TR-10 North Cyprus Mersin Turkey

In this paper the design of recognition system for retinal images using neural network is considered. Retina based recognition is perceived as the most secure method for identification of an identity used to distinguish individuals. The retina recognition stages including retina image acquisition, feature extraction and classification of the features are discussed. The structure of the neural network based retina identification is presented. Training of neural network based recognition system is performed using backpropagation algorithm. The structure of neural networks used for retina recognition and its learning algorithm are described. The implementation of recognition system has been done using MATLAB package. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Neural network retina recognition backpropagation algorithm

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