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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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Neural Network Based Electronic Nose for Discrimination of Fish Sauces

Neural Network Based Electronic Nose for Discrimination of F...

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11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Nimsuk, Nitikarn Thammasat Univ Fac Engn Dept Elect & Comp Engn Pathum Thani Thailand

ISBN: (纸本)9781479929931

Fish sauce is one of the signature condiments in various cuisines in many countries. In this study, fish sauces are successfully discriminated depending on their quality indicated by the level of Total Nitrogen (TN) content. We introduce an electronic nose (E-nose) technology to measure the odor of fish sauce. Feature extraction methods are also performed in order to obtain information relevant to the odor discrimination task. Four features based on both steady-state and transient responses are extracted from each response signal obtained from the E-nose. The principal component analysis (PCA) is also introduced to reduce the dimensionality of feature vector for avoiding the curse of dimensionality. The backpropagation (BP) learning algorithm is then used for the discrimination. Using this method, we achieve high discrimination performance to the fish sauces.

关键词： electronic nose neural network fish sauce principal component analysis backpropagation algorithm

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Application of Neural Network for Digital Recursive Filter Design 10

Application of Neural Network for Digital Recursive Filter D...

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10th IEEE International Conference on Application of Information and Communication Technologies (AICT)

作者： Allakhverdiyeva, Naila Baku Higher Oil Sch Proc Automat Engn Dept Baku Azerbaijan

ISBN: (纸本)9781509018413

Digital Signal Processing is an advanced technology that will determine the direction of science and technology in the next centuries. One of the main direction of digital signals processing is digital filters, which in the most cases have advantages over analog filters. Currently there are various methods of filter analysis and design. In this work, for synthesis of all types of recursive filters (low pass, high pass, bandwidth, band stop) is used a neural network. The main objective of filter synthesis is to find the filter coefficients. These filter coefficients define the filter transfer function. Using an iterative procedure of the neural network such as backpropagation algorithm, on base of Visual C++ software was developed the program, which designs recursive filters with required characteristics. This is particularly important for the designing of the new correcting filters characteristics, the purpose of which is to reduce the unwanted noises in the measurement signal.

关键词： Digital filter IIR filter neural network backpropagation algorithm Impulse response Transfer function

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Implementation of Multimodal Biometric Authentication Using Soft Computing Techniques

Implementation of Multimodal Biometric Authentication Using ...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Gunasekaran, K. Mahalakshmi, P. Pavendar Bharathidasan Coll Engg & Tech Dept CSE Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479938346

Identity Management refers to a challenge of providing authorized users with secure and easy access to information and services for personal identification system. The objective is to implement a secure identification system for determining an individual's identity. Conventional methods for personal identification include keys, tokens, access cards, PINs and passwords which leads to drawbacks such as stealing, duplicating, cracking and sharing. In order to overcome the drawbacks, biometrics based identification is implemented. Nevertheless, unimodal biometrics is suffered due to noise, intra class variations, spoof attacks, non-universality. To avoid these attacks, multimodal biometrics which is the combination of more modalities is adapted. Fingerprint and Palmprint identification are the emerging and leading technologies used as a source of authentication. Neural Networks one of the Soft Computing techniques has been proposed for feature extraction and minutiae point detection. Euclidean distance values are used for minutiae matching. The proposed method achieves excellent identification rate and are more secure in noisy environment.

关键词： Biometrics 2D-Gabor Filter K-Nearest Neighborhood algorithm(KNN) Artificial Neural Networks(ANN) Feed Forward Neural Networks(FNN) MultilayerPerceptron(MLP) backpropagation algorithm Euclidean distance

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Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models

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MARITIME ECONOMICS & LOGISTICS 2021年第2期23卷 310-327页

作者： Munim, Ziaul Haque Schramm, Hans-Joachim Univ South Eastern Norway Fac Technol Nat & Maritime Sci Horten Norway Univ Agder Sch Business & Law Kristiansand Norway Vienna Univ Econ & Business Inst Transport & Logist Management Vienna Austria Copenhagen Business Sch Dept Operat Management Copenhagen Denmark

Major players in maritime business such as shipping lines, charterers, shippers, and others rely on container freight rate forecasts for operational decision-making. The absence of a formal forward market in container shipping necessitates reliance on forecasts, also for hedging purposes. To identify better performing forecasting approaches, we compare three models, namely autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) or vector error correction (VEC), and artificial neural network (ANN) models. We examine the China Containerized Freight Index (CCFI) as a collection of weekly freight rates published by the Shanghai Shipping Exchange (SSE) for four major trade routes. We find that, overall, VAR/VEC models outperform ARIMA and ANN in training-sample forecasts, but ARIMA outperforms VAR and ANN taking test-samples. At route level, we observe two exceptions to this. ARIMA performs better for the Far East to Mediterranean route, in the training-sample, and the VEC model does the same in the Far East to US East Coast route in the test-sample. Hence, we advise industry players to use ARIMA for forecasting container freight rates for major trade routes ex-China, except for VEC in the case of the Far East to US East Coast route.

关键词： Artificial neural networks Vector error correction Forecast performance Ocean freight rates backpropagation algorithm Diebold-Mariano test

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Background Compensation of Static TI-ADC Nonlinearities in Coherent Optical Receivers

Background Compensation of Static TI-ADC Nonlinearities in C...

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Argentine Conference on Electronics (CAE)

作者： Fernandez Bocco, Alvaro Solis, Fredy Reyes, Benjamin T. Morero, Damian A. Hueda, Mario R. Fdn Fulgor RA-5000 Cordoba Argentina Univ Nacl Cordoba CONICET Lab Comunicac Digitales RA-5000 Cordoba Argentina

ISBN: (纸本)9781728175799

This paper presents a novel background compensation technique of nonlinear distortions in time-interleaved analog-to-digital converters (TI-ADC). The proposed scheme is based on the well-known error backpropagation algorithm, which has been extensively used in machine learning applications. The technique enables adaptive compensation using low-complexity hardware such as look-up tables. The effectiveness and intrinsic background characteristics of this proposal make it a well suited candidate for application in high-speed, high modulation order digital communication receivers. Simulation results in a coherent optical receiver demonstrate that the proposed background compensation algorithm is able to practically eliminate the penalties caused by the TI-ADC nonlinearity.

关键词： backpropagation algorithm nonlinearity compensation coherent optical receiver TI-ADC

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Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2016年第4期2卷 1-10页

作者： Wagh, Vasant Madhav Panaskar, Dipak Baburao Muley, Aniket Avinash Mukate, Shrikant Vitthal Lolage, Yogesh Popatrao Aamalawar, Manesh Laxman Swami Ramanand Teerth Marathwada Univ Sch Earth Sci Nanded Maharashtra India Swami Ramanand Teerth Marathwada Univ Sch Math Sci Nanded Maharashtra India

This study presents an artificial neural network (ANN) model predicting values of sodium adsorption ratio (SAR), residual sodium carbonate, magnesium adsorption ratio, Kellys ratio and percent sodium (% Na) in the groundwater of Nanded tehsil. The 50 groundwater samples were analyzed for different physicochemical parameters such as pH, EC, TDS, Ca, Mg, Na, K, Cl, CO3, HCO3, SO4 and NO3, for the pre monsoon season 2012. The ANN model is developed through R programming and compared with MS-Excel software. These parameters were used as input variables in the ANN based groundwater quality indices for irrigation suitability prediction. The best back propagation algorithm and neuron numbers were determined for optimization of the model architecture. The resilient backpropagation algorithm with weight back tracking was used for optimization of seven neurons through sensitive analysis. It showed that a network with seven neurons was highly accurate in predicting the irrigation suitability indices. The relative mean squared error, coefficient of determination (R-2) and mean absolute relative error between experimental data and model outputs were calculated. It is observed that is a good agreement between actual data and ANN outputs of groundwater for irrigation suitability indices for training and testing datasets. The spatial distribution maps of measured and predicted values of irrigation indices were prepared using ArcGIS software. Hence, the result confirms that the ANN model is an applied tool to predict the groundwater suitability for irrigation purpose in Nanded tehsil.

关键词： Neural network backpropagation algorithm Irrigation suitability Nanded tehsil

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Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

Multinodal Load Forecasting in Power Electric Systems using ...

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1st International Conference on High Performance Structures and Materials Engineering

作者： Altran, Alessandra Bonato Minussi, Carlos Roberto Martins Lopes, Mara Lucia Chavarette, Fabio Roberto Peruzzi, Nelson Jose UNESP Univ Estadual Paulista Fac Engn Dept Elect Engn Ave Jose Carlos Rossi 1370 BR-15385000 Ilha Solteira SP Brazil UNESP Fac Engn Dept Math BR-15385000 Avenida SP Brazil UNESP Dept Exact Sci BR-13484900 Avenida SP Brazil

ISBN: (纸本)9783037850756

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

关键词： Multinodal Forecast of Electric Load Artificial Neural Networks backpropagation algorithm Radial Basis Function

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Nonlinear system identification using discrete-time neural networks with stable learning algorithm

Nonlinear system identification using discrete-time neural n...

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5th International Conference on Informatics in Control, Automation and Robotics

作者： Korkobi, Talel Djemel, Mohamed Chtourou, Mohamed XYZ Univ Inst Problem Solving Natl Engn Sch Sfax ENIS BP W Sfax 3038 Tunisia ENIS Natl Engn Sch Sfax Design & Optimizat Complex Syst Sfax 3038 Tunisia

ISBN: (纸本)9789898111302

This paper presents a stable neural sytem identification for nonlinear systems. An input output discrete time representation is considered. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomenon during the learning process is a-voided. A Lyapunov analysis is made in order to extract the new updating formulation which contain a set of inequality constraints. In the constrained learning rate algorithm, the learning rate is updated at each iteration by an equation derived using the stability conditions. As a case study, identification of two discrete time systems is considered to demonstrate the effectiveness of the proposed algorithm. Simulation results concerning the considered systems are presented.

关键词： stability neural networks identification backpropagation algorithm constrained learning rate Lyapunov approach

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Neural Network Models for Prediction of Evaporation Based on Weather Variables 2nd

Neural Network Models for Prediction of Evaporation Based on...

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2nd International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Rakhee Singh, Archana Kumar, Amrender Amity Univ CSE Noida India ICAR IARI Agr Knowledge Management Unit Delhi India

ISBN: (纸本)9789811331404;9789811331398

Artificial Neural networks (ANNs) is a computation method that can be utilized for predictions. In this study prediction of evaporation using ANN's multilayer perceptron (MLP) is attempted considering different weather variables viz. Relative Humidity Morning & Evening, Bright Sunshine Hours, Rainfall, Maximum & Minimum temperature, Mean Temperature and Mean Relative Humidity. The analysis is done over different parts of India viz. Raipur, Pantnagar, Karnal, Hyderabad and Samastipur. Weather of four lag weeks from week of forecast is considered for the model development. The lag periods were also utilized to develop weather indices. Subsequent two years were not included while developing the model for predicting evaporation for different locations. The performance of the developed models was evaluated based on Root Mean Square Error (RMSE).

关键词： Artificial Neural Networks Prediction models Weather indices backpropagation algorithm Mean absolute percentage error

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