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检索条件"主题词=Back propagation algorithm"

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Classification of Bio-optical signals using soft computing tools

Classification of Bio-optical signals using soft computing t...

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9th International Conference on Software Engineering Artificial Intelligence, Networking and Parallel/Distributed Computing

作者： Nayak, G. Subramanya Puttamadappa, C. Kamath, Akshata Sudeep, B. Raja Kavitha, K. Manipal Univ Manipal Inst Technol E&C Engn E&C Engn Manipal Karnataka India SJB Inst Technol E&C Engn Bangalore 560087 Karnataka India

ISBN: (纸本)9780769532639

The identification of the state of human skin tissues is discussed here. The Bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is filttered and normalized. Then different features like Skewness, summation, median residuals, power spectral density, etc were extracted. The values of the feature vector reveal information regarding tissue state. The values of the feature vector reveal information regarding tissue state. These parameters have been analyzed for discrimination between normal and pathology conditions. For analysis, a specific data set has been considered. Further discrimination between normal and pathology spectra is also be achieved by using MATLAB @6.1 tool based classical multilayer feed forward neural network with back propagation algorithm.

关键词： artificial neural network principal component analysis back propagation algorithm

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Artificial Neural Network Based E-Nose And Their Analytical Applications In Various Field

Artificial Neural Network Based E-Nose And Their Analytical ...

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Jamal, Maria Khan, M. R. Imam, S. A. Jamal, Arif GGSIPU IGIT ECE Dept Delhi 6 India JMI ECE Dept Delhi 25 India CPWD Delhi 01 India

ISBN: (纸本)9781424478132

A brief and historical overview of research and development in the field of artificial neural network based electronic nose system is presented. Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Electronic/artificial noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. An electronic nose is generally composed of a chemical sensing system (e. g., sensor array or spectrometer) and a pattern recognition system (e. g., artificial neural network). We are developing electronic noses for the automated identification of volatile chemicals for environmental, medical applications, commercial industries, including the agricultural, biomedical, cosmetics, food, manufacturing, military, pharmaceutical, regulatory applications and various scientific research fields. This paper is a review of the major electronic nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man.

关键词： Artificial Neural Network back propagation algorithm multilayer Feed-forward network e-nose

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An Inverse Halftoning algorithm Based on Neural Networks and UP(x) Atomic Function 38

An Inverse Halftoning Algorithm Based on Neural Networks and...

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38th International Conference on Telecommunications and Signal Processing (TSP)

作者： Pelcastre-Jimenez, Fernando Nakano-Miyatake, Mariko Toscano-Medina, Karina Sanchez-Perez, Gabriel Perez-Meana, Hector Inst Politecn Nacl Mech & Elect Sch Mexico City 04430 DF Mexico Inst Politecn Nacl Mech & Elect Engn Sch Mexico City 04430 DF Mexico

ISBN: (纸本)9781479984985

Halftoning and inverse halftoning algorithms are very important image processing tools that have been widely used in digital printers, scanners, steganography and image authentication systems. Because such applications require obtaining high quality gray scale images from its halftoning versions, several inverse halftoning algorithms have been proposed during the last several years, which provide gray scale images with Peak Signal to Noise Ratio (PSNR) of about 25 to 28 dB. Although this may be enough for several applications, exist several other that require higher image quality. To this end, this paper proposes an inverse halftoning algorithm based on Upx atomic function and multilayer perceptron neural network. Experimental results show that proposed scheme provides gray scale images with PSNRs higher than 30dB independently of the method used to generate the halftone image.

关键词： Halftoning inverse halftone UP(x) atomic function multilayer perceptron back propagation algorithm

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Soft Sensor for Flame Temperature Measurement and IoT based Monitoring in Power Plants 2nd

Soft Sensor for Flame Temperature Measurement and IoT based ...

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2nd International Conference on Large Area Flexible Microelectronics (ILAFM) - Wearable Electronics

作者： Sujatha, K. Bhavani, Nallamilli P. G. Cao, Su-Qun Kumar, K. S. Ram Dr MGR Educ & Res Inst Mech & Mat Engn Fac Madras Tamil Nadu India Dr MGR Educ & Res Inst CEAIR Dept EEE ECE Madras Tamil Nadu India Huaiyin Inst Technol Huaian Jiangsu Peoples R China JNN Inst Engn Madras Tamil Nadu India

Investigation of temperature measurement from the flame colour in thermal and gas turbine power plants is of enormous significance in the realm of vision machine technology. The primary objective for this work relies on detection, recognition and understanding of colour image processing for flame colour analysis. In this effort, soft computing methods using Artificial Neural Network (ANN) model with back propagation algorithm (BPA) and Ant Colony Optimisation (ACO) are used for this purpose. The central theme of this work uses the fact that the colour of the flame images is dependent on the temperature. The initial move is to describe a facet quantity for each flame image together with 10 facet rudiments, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid about X and Y, orientation and the two discriminant vectors correspondingly. The superiority of the images used is improved using Curvelet transform. The conception of flame detection and classification is conceded to compute the temperature from its colour. The specimen incorporates 51 flame images, a portion of which is used for trail and testing the ANN and ACO model. Ultimately, the whole specimen flame images are recognized and classified based on the temperatures corresponding to the core of the fire ball. The results are being validated by comparing with the conventional Euclidean classifier. Demonstrations establish an effective and indigenous system for flame temperature measurement. The elucidation states that the Internet of Things (IoT) with the proposed intelligent temperature sensor is connected to the embedded computing system to monitor the fluctuation in flame temperature with respect to colour changes in order to ensure complete combustion. This scheme utilizes wearable electronics technology which constantly monitors and controls the improvement of productivity in p

关键词： Soft Sensor back propagation algorithm Ant Colony Optimization Euclidean classifier Flame Temperature Feature extraction Curvelet transform Fisher's Linear Discriminant Analysis Arduino UNO and Internet of Things

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AUTOMATIC FACIAL EXPRESSION RECOGNITION SYSTEM

AUTOMATIC FACIAL EXPRESSION RECOGNITION SYSTEM

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International Conference on Computing, Communications and Networking

作者： Balasubramani, A. Kalaivanan, K. Karpagalakshmi, R. C. Monikandan, R. Bharathidasan Univ II M Tech IT Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781424435944

The paper is mainly about the idea of robots replacing humans in all aspects. Able to express is the only aspect that makes humans a step higher than the robots we produce. We project an idea where robot can mimic human expressions by means of the "automatic facial expression system". Automatic facial expression recognition system that utilizes multi-stream Hidden Markov Models (NEURAL NETWORKS). The proposed system uses Facial Animation Parameters (FAPs), supported by the MPEG-4 standard, as features describing facial expressions. In particular, the FAPs controlling the movement of the outer-lips and eyebrows are used as visual features for classification. Experiments were performed under several different scenarios utilizing outer-lip and eyebrow FAPs individually and jointly. A new approach is proposed for introducing facial expression and FAP group dependent stream weights. The weights were chosen based on the facial expression recognition results obtained when FAP group streams are utilized individually. The proposed multi stream NEURAL NETWORKS facial expression recognition system achieves relative reduction of the expression recognition error of 44%, compared to the single-stream NEURAL NETWORKS system.

关键词： Neural Networks Facial Expressions Image processing back propagation algorithm

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POSITION CONTROL of a DC MOTOR USED in SOLAR PANELS WITH ARTIFICIAL NEURAL NETWORK

POSITION CONTROL of a DC MOTOR USED in SOLAR PANELS WITH ART...

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11th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Sahin, Murat Bulbul, H. Ibrahim Colak, Ilhami Gazi Univ Fac Technol Renewable Energy Res Grp GURER Ankara Turkey Gazi Univ Fac Ind Art & Educ Renewable Energy Res Grp GURER Ankara Turkey

ISBN: (纸本)9780769549132

In this study, it is aimed to model a de motor which will be able to achieve panel movement in solar panels and realize position control using artificial neural networks method in Laview. Position control of DC motors is possible with position sensors integrated to motors or external position sensors. The most popular of these sensors are encoder and transducer. Within this study, by using the LabView program in the computer environment, DC motor model was created and simulations were done on this model. In simulations, the motor current, position and velocity were measured from motor model. The Current and position data collected from simulations was used as the closed-loop feedback control system. As the control system, artifical neural network (ANN) was used due to the nonlinear structure of the current data.

关键词： DC Motor Artificial Neural Network back propagation algorithm Simulation

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Double Circuit Transmission Lines Short Circuit Fault Location Using Wavelet Transform and MLP 25

Double Circuit Transmission Lines Short Circuit Fault Locati...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Hosseini, Kamran Tayyebi, S. Asghar Ahmadian, Mohammad Bagher TANESH North Power Transmiss Maintenance CO Sari Iran

ISBN: (纸本)9781509059638

the present paper introduces a novel hybrid framework able to identify and locate fault on double circuit transmission lines without being influenced by the mutual coupling and noise. The proposed algorithm shows a new separation method in order to detect the circuit affected by fault using Fourier wavelet transform analysis on current waveforms. For such lines faults classification and location employing voltage waveforms and wavelet transform mechanism on current waveforms in post-fault half cycle, a pattern recognition approach is introduced through neural network. Under certain faults, the proposed method has been tested with a variety of location, resistance and fault inception angle. The studies derived results based on NN indicated that the proposed method is capable to improve the conventional relays algorithm performance.

关键词： Fourier Transform wavelet transform ANN MLP back propagation algorithm fault classification fault location transmission lines

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A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus

A Neural Network Based Expert System for the Diagnosis of Di...

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International Conference on Information Technology Science (MosITS)

作者： Alade, Oluwatosin Mayowa Sowunmi, Olaperi Yeside Misra, Sanjay Maskeliunas, Rytis Damasevicius, Robertas Covenant Univ Ota Nigeria Kaunas Univ Technol Kaunas Lithuania

ISBN: (纸本)9783319749808;9783319749792

Diabetes is a disease in which the blood glucose, or blood sugar levels in the body are too high. The damage caused by diabetes can be very severe and even more pronounced in pregnant women due to the tendency of transmitting the hereditary disease to the next generation. Expert systems are now used in medical diagnosis of diseases in patients so as to detect the ailment and help in providing a solution to it. This research developed and trained a neural network model for the diagnosis of diabetes mellitus in pregnant women. The model is a four-layer feed forward network, trained using back-propagation and Bayesian Regulation algorithm. The input layer has 8 neurons, two hidden layers have 10 neurons each, and the output layer has one neuron which is the diagnosis result. The developed model was also incorporated into a web-based application to facilitate its use. Validation by regression shows that the trained network is over 92% accurate.

关键词： Expert system Diabetes diagnosis Neural network back propagation algorithm

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An Effective Approach of Feature Selection for Recommender Systems using Fuzzy C Means clustering along with Ant Colony Optimization and Neural Networks 8

An Effective Approach of Feature Selection for Recommender S...

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8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Hasija, Hitesh VISA Consolidated Support Serv India Private Ltd Bangalore Karnataka India

ISBN: (纸本)9781509030385

Ratings provided by user for a movie or TV program also depends on demographic information, day type, time, mood, and other factors of environment. Thus, context aware recommender systems are used for providing recommendations. As recommender systems suffers from cold start, sparse data and scalability problems. Therefore, artificial neural networks (ANN) are used to solve these problems. But, training of ANN again takes a very large time, due to high dimensional features. Hence, feature subset selection is done to solve this problem. But, it is a kind of combinatorial optimization problem, which could be solved by using Ant Colony Optimization (ACO). ACO with heuristic function as fuzzy values obtained after applying fuzzy c means clustering algorithm over movie lens data set provided optimum results. back propagation algorithm has been used, for the training of neural network, only on those features which are obtained after applying modified ACO along with roulette wheel selection algorithm. Results are analyzed by providing ratings of those movies which are not provided in "movie lens data set" yet, as a solution to cold start problem. Finally, the accuracy of recommender systems is calculated by determining mean absolute error and comparison is provided with other previous approaches for different data sets.

关键词： Feature subset selection problem Ant colony optimization artificial neural networks Fuzzy c means clustering back propagation algorithm Roulette wheel selection

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Introduction to Neural Network and Improved algorithm to Avoid Local Minima and Faster Convergence

Introduction to Neural Network and Improved Algorithm to Avo...

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1st International Conference on Computational Intelligence and Information Technology (CIIT 2011)

作者： Bari, Ayesha Bhasin, Kavleen Karnawat, Darshan N. GBPUA&T Coll Technol Pantnagar UA India DKTE Soc Textile & Engn Inst Dept Elect Ichalkaranji MS India

ISBN: (纸本)9783642257339

Neural network is a massively parallel distributed processor inspired by the structure and functional aspect of biological neural network that employ learning algorithm, like back propagation for computation .back propagation follows supervised learning rule for processing data. The architecture is complex and processing confronts problems like local minima, slow convergence and premature saturation. We have introduced improved neuron model and learning rule like multiplicative neuron model using extra term in algorithm called a proportional factor. This along with other modifications in network architecture, that helps attain faster learning and accurate computation.

关键词： Supervised Learning back propagation algorithm Premature Saturation Differential Adaptive Learning and Multiplicative Neuron Model

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