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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 Prediction of a Hydrocarbon Degradation Process in Biopiles 1

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IFAC Proceedings Volumes 2004年第3期37卷 287-292页

作者： R. De la Torre Sánchez I. Baruch J. Barrera-Cortés CINVESTAV-IPN Department of Biotechnology and Bioengineering Av. Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco México D.F. C.P. 07360 Fax: (+52-5)747-33-13 CINVESTAV-IPN Department of Automatic Control Av. Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco México D.F. C.P. 07360 Fax: (+52-5)747-33-13

This paper proposes the use of a Recurrent Neural Network model (RNN) for modeling a hydrocarbon degradation process carried out in a biopile system. The proposed RNN model has seven inputs, five outputs and twelve neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the backpropagation through time. The approximation and generalization error is below 2%. The learning was performed in 131 epochs, 56 iterations each one

关键词： Neural networks backpropagation algorithm biotechnology hydrocarbon degradation biopile system

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New learning automata based algorithms for determining the number of input weights of hidden layers in a multi layer neural networks

Amirkabir (Journal of Science and Technology)

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Amirkabir (Journal of Science and Technology) 2004年第58 A期15卷 286-319页

作者： Mashoufi, B. Mevbodi, M.R. Motamedi, Sayed A. Menhaj, M.B. Electrical Engineering Department Uremia University Iran Computer Engineering Department Amirkabir University of Technology Iran Electrical Engineering Department Amirkabir University of Technology Iran

backpropagation algorithm performs gradient descent only in the weight space of a network with fixed topology. The Number of layers, Neurons and network weights have important influence on network performance. So algorithms that can find appropriate network architecture automatically are thus highly desirable. Researchers have proposed different algorithms for determining optimum size of neural networks. Meybodi and Beigy introduced the first learning automata based algorithms, called WSA algorithm. This algorithm by turning off the unimportant weights, not only reduces network complexity but also increases network generalization ability. At the beginning, all weights of the network are on and contribute to learning. The on weights whose absolute values are less than a threshold value, are penalised and those whose absolute value are larger than another threshold value, are rewarded. The on weights, whose absolute values lie between these two threshold values, neither rewarded, nor penalized. By choosing optimum values for these values we can obtain, networks with minimum number of weights which can learn training patterns with acceptable error and generalization ability. In this paper we introduce a new learning automata based algorithm, called AWSA for adaptation of parameters of WSA algorithm. Also, a new algorithm called MWSA is introduced to determine important weights in the multi layer neural networks. These algorithms are applied to number of problems such as recognition of English number. Persian printed numbers recognition, second order discrete time nonlinear function approximation and Persian phoneme recognition. The results obtained show that the proposed algorithms have better performance than other existing algorithms.

关键词： backpropagation algorithm Learning Automata Multi layer Neural Networks Neural Networks Engineering Neural Networks Topology

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A new digital pulse-mode neuron with adjustable activation function

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2003年第1期14卷 236-242页

作者： Hikawa, H Oita Univ Dept Comp Sci & Intelligent Syst Oita 8701192 Japan

This paper describes a new pulse-mode digital neuron which is based on voting neuron. The signal level of the neuron is represented by frequency of pulse signals. The proposed neuron provides adjustable nonlinear function,. which resembles the sigmoid function. The proposed neuron and experimental multilayer neural network (MNN) are implemented on field programmable gate array (FPGA) and various experiments are conducted to test the performance of the proposed system. The experimental results show that the proposed neuron has rigid adjustable nonlinear function.

关键词： backpropagation algorithm field-programmable gate array (FPGA) multilayer neural network (MNN) on-chip learning pulse-mode operation

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Classification of Venezuelan spirituous beverages by means of discriminant analysis and artificial neural networks based on their Zn, Cu and Fe concentrations

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TALANTA 2003年第6期60卷 1259-1267页

作者： Hernández-Caraballo, EA Avila-Gómez, RM Capote, T Rivas, F Pérez, AG Univ Los Andes Fac Ciencias IVAIQUIM Lab Espectroscopia Mol Merida 5101 Venezuela Univ Ctr Occidental Lisandro Alvarado Dept Quim & Suelos Unidad Anal Instrumental Barquisimeto 3001 Venezuela Univ Los Andes Fac Ingn Escuela Ingn Sistemas Lab Sistemas Inteligentes Merida 5101 Venezuela Univ Los Andes Fac Ciencias Econ & Sociales Inst Estadist Aplicada & Computac Merida 5101 Venezuela

Copper, zinc and iron concentrations were determined in "aguardiente de Cocuy de Penca" (Cocuy de Penca Firewater), a spirituous beverage very popular in the North-Western region of Venezuela, by flame atomic absorption spectrometry (FAAS). These elements were selected for their presence can be traced to the (illegal) manufacturing process of the aforementioned beverages. Linear and quadratic discriminant analysis (QDA), and artificial neural networks (ANNs) trained with the backpropagation algorithm were employed for estimating if such beverages can be distinguished based on the concentrations of these elements in the final product, and whether it is possible to assess the geographic location of the manufacturers (Lara or Falcon states) and the presence or absence of sugar in the end product. A linear discriminant analysis (LDA) performed poorly, overall estimation and prediction rates being 51.7% and 50.0%, respectively. A QDA showed a slightly better overall performance, yet unsatisfactory (estimation: 79.2% prediction: 72.5%). Various ANNs, comprising a linear function (L) in the input layer, a sigmoid function (S) in the hidden layer(s) and a hyperbolic tangent function (T) in the output layer, were evaluated. Of the networks studied, the (3L:5S:7S:4T) gave the highest estimation (overall: 96.5%) and prediction rates (overall: 97.0%), demonstrating the superb performance of ANNs for classification purposes. (C) 2003 Elsevier B.V. All rights reserved.

关键词： iron copper zinc classification Cocuy spirituous beverages linear discriminant analysis (LDA) quadratic discriminant analysis (QDA) artificial neural networks (ANNs) backpropagation algorithm

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A novel approach to diagnose diabetes based on the fractal characteristics of retinal images

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2003年第3期7卷 163-170页

作者： Cheng, SC Huang, YM Natl Cheng Hung Univ Dept Engn Sci Tainan 701 Taiwan

A novel diagnostic scheme to develop quantitative indexes of diabetes is introduced in this paper. The fractal dimension of the vascular distribution is estimated because we discovered that the fractal dimension of a severe diabetic patient's retinal vascular distribution appears greater than that of a normal human's. The issue of how to yield an accurate fractal dimension is to use high-quality images. To achieve a better image-processing result, an appropriate image-processing algorithm is adopted in this paper. Another important fractal feature introduced in this paper is the measure of lacunarity, which describes the characteristics of fractals that have the same fractal dimension but different appearances. For those vascular distributions in the same fractal dimension, further classification can be made using the degree of lacunarity. In addition to the image-processing technique, the resolution of original image is also discussed here. In this paper, the influence of the image resolution upon the fractal dimension is explored. We found that a low-resolution image cannot yield an accurate fractal dimension. Therefore, an approach for examining the lower bound of image resolution is also proposed in this paper. As for the classification of diagnosis results, four different approaches are compared to achieve higher accuracy. In this study, the fractal dimension and the measure of lacunarity have shown their significance in the classification of diabetes and are adequate for use as quantitative indexes.

关键词： backpropagation algorithm classification diagnosis system fractal dimension genetic algorithm image processing lactmarity radial basis function network voting scheme

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The solutions of vibration control problems using artificial neural networks

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2003年第5期340卷 307-325页

作者： Alli, H Uçar, A Demir, Y Firat Univ Fac Engn Dept Mech Engn Elazig Turkey Firat Univ Fac Engn Dept Elect & Elect Engn Elazig Turkey

This paper introduces an alternative method artificial neural networks (ANN) used to obtain numerical solutions of mathematical models of dynamic systems, represented by ordinary differential equations (ODEs) and partial differential equations (PDEs). The proposed trial solution of differential equations (DEs) consists of two parts: The initial and boundary conditions (BCs) should be satisfied by the first part. However, the second part is not affected from initial and BCs, but it only tries to satisfy DE. This part involves a feedforward ANN containing adjustable parameters (weight and bias). The proposed solution satisfying boundary and initial condition uses a feedforward ANN with one hidden layer varying the neuron number in the hidden layer according to complexity of the considered problem. The ANN having appropriate architecture has been trained with backpropagation algorithm using an adaptive learning rate to satisfy DE. Moreover, we have, first, developed the general formula for the numerical solutions of nth-order initial-value problems by using ANN. For numerical applications, the ODEs that are the mathematical models of linear and nonlinear mass-damper-spring systems and the second- and fourth-order PDEs that are the mathematical models of the control of longitudinal vibrations of rods and lateral vibrations of beams have been considered. Finally, the responses of the controlled and non-controlled systems have been obtained. The obtained results have been graphically presented and some conclusion remarks are given. (C) 2003 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.

关键词： vibration control problems artificial neural networks backpropagation algorithm

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Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

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ECOLOGICAL MODELLING 2003年第3期160卷 265-280页

作者： Park, YS Céréghino, R Compin, A Lek, S Univ Toulouse 3 CNRS UMR 5576 CESAC F-31062 Toulouse France

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour-Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP),. a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded,to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a, set of four environmental variables, showed a high accuracy (r = 0.91 and r = 0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to. assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables: (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： self-organizing-map backpropagation algorithm ordination classification prediction aquatic insects species richness

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A hierarchical neuro-fuzzy system based on S-implications

A hierarchical neuro-fuzzy system based on S-implications

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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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Study on range interval distance of prestressed anchor bars using update backpropagation neural network

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Journal of Coal Science & Engineering(China) 2003年第2期9卷 35-39页

作者：吴顺川张友葩高永涛 School of Civil and Environmental Engineering

Taking the practical reinforced engineering of a reinforced soil retaining wall as an example, which located in Shandong Province and set on 104 national highway, the stress spread behaviors of the anchor bars in the preforced proceeding were tested. According to the test data, and by use of the update backpropagation (BP) algorithm neural network(NN), the test method and it’s mechanism were studied by the network, then the learning results show the mean square error(MSE) only at the 2 55% level, and the proof testing results show the MSE at 4 38% level (the main aim is to build a NN directly from the in situ test results (the learning phase)). Ipso facto, the learning and adjustment abilities of the NN permit us to develop the test data, subsequently, 36 test data were acquired from the NN. By use of the provide data, as well as the failure situation and carried loading capacity of the retaining wall, finally, the choice the reasonable range interval distance of prestress cement grouting anchor bars were carried out, and the result was 2 m×2 m.

关键词： prestressed anchor bar range interval distance NN backpropagation algorithm

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