kohonen and counterpropagation neural networks employed for modeling endocrine disruptors
Báo cáo hóa học: " Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks" ppt
... train and test our classifiers: 80% of the patterns were used for training, and 20% of the patterns were used for testing (see Table 3) Table shows the optimal network structure and parameters for ... Conference on Industrial Informatics, INDIN, pp 510–515 (2009) Page 11 of 11 37 D Andina, J Sanz-Gonzalez, On the problem of binary detection with neural networks, in Circuits and Systems Proceedings, ... considered for image segmentation, because of the great similarity between segmentation and clustering, although clustering was developed for feature space, whereas segmentation was developed for the...
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Training issues and learning algorithms for feedforward and recurrent neural networks
... 1 TRAINING ISSUES AND LEARNING ALGORITHMS FOR FEEDFORWARD AND RECURRENT NEURAL NETWORKS TEOH EU JIN B.Eng (Hons., 1st Class), NUS A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY ... feedforward and recurrent neural networks, and how does neural structure influence the efficacy of the learning algorithm that is applied? When are recurrent architectures preferred over feedforward ... inject diversity and variety in my thinking and outlook, and whose diligence and enthusiasm has always made the business of teaching and research such a pleasant and stimulating one for me Credit...
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static and dynamic neural networks from fundamentals to advanced theory
... 85 88 94 95 STATIC NEURAL NETWORKS Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms 4.1 Two-Layered Neural Networks 4.1.1 Structure and Operation Equations ... Theorem and its Feedforward Networks 7.1.1 Basic Definitions 7.1.2 Stone-Weierstrass Theorem and Approximation 7.1.3 Implications for Neural Networks 7.2 Trigonometric Function Neural Networks ... Proof for Two-Layered Networks 7.3.2 Approximation Using General MFNNs 7.4 Kolmogorov's Theorem and Feedforward Networks 7.5 Higher-Order Neural Networks (HONNs) 7.6 Modified Polynomial Neural Networks...
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Báo cáo hóa học: " Research Article Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition" pot
... pairwise and standard multiclass neural networks were implemented in Matlab, using neural networks Toolbox The pairwise classifiers and the multiclass networks include hidden and output layers For ... B, and Faces94 were 64 × 64, 32 × 32, and 45 × 50 pixels, respectively For these face image sets, the number of classes and number of samples per subject were 40 and 10, 38 and 60, and 150 and ... performance was achieved with two hidden neurons, while for the multiclass networks the numbers of hidden neurons were dependent on problems and ranged between 25 and 200 The best performance for...
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Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays
... Ra stands for roughness average value, typically measured in micrometers (lm), lm stands for the sampling length of the prole, and |y(x)| stands for the absolute measured values of the peak and ... an example of a traditional theoretical model where Ra stands for roughness average (in lm), f stands for feed (in mm/rev), and r stands for tool nose radius (in mm) Ra $ 0:032x f2 r 2ị Such models, ... C2 C3 d ị 3ị In Eq (3), Ra stands for roughness average V, f, and d stand for cutting speed (m/min), feed (mm/rev), and depth of cut (mm), respectively C0, C1, C2, and C3 are constants that must...
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Tài liệu Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations pptx
... Equation (16.15) and then assigned a fuzzy membership function The divisions of the input and output spaces are shown in Figure 16.1, where N is for x1, and for x2 and y The width for each variable ... important and may be required for further development of ISRR technology for the next century References Armarego, E J A and Deshpande, N P., 1989, Computerized predictive cutting models for forces ... ISRR-ANN 4-5-1, and ISRR-ANN 4-7-7-1 models are 95.78%, 95.87%, and 99.27%, respectively 16.5.2 Conclusions The fuzzy logic and neural- networks- based ISRR models demonstrated that learning and reasoning...
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neural networks for instrumentation, measurement and related industrial applications
... the neural networks for sensors and measurement systems, for identification in instrumentation and measurement, for instrumentation and measurement dedicated to system and plant control, and for ... bearings Neural networks in manufacturing Neural networks for bearing fault diagnosis Conclusions Neural Networks for Measurement and Instrumentation in Robotics, Mel Siegel Instrumentation and measurement ... practical issues Case studies: neural networks for instrumentation and measurement systems in robotic applications in research and industry Neural Networks for Measurement and Instrumentation in Laser...
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Báo cáo hóa học: " Research Article Existence and Stability of Antiperiodic Solution for a Class of Generalized Neural Networks with Impulses and Arbitrary Delays on Time Scales" ppt
... impulses and arbitrary delays This class of generalized neural networks include many continuous or discrete time neural networks such as, Hopfield type neural networks, cellular neural networks, ... Cohen-Grossberg neural networks, and so on To the best of our knowledge, the known results about the existence of anti-periodic solutions for neural networks are all done by a similar analytic method, and ... Hopfield neural networks with impulses and delays,” Journal of Computational and Applied Mathematics, vol 224, no 2, pp 602–613, 2009 K Li, “Stability analysis for impulsive Cohen-Grossberg neural networks...
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báo cáo hóa học:" Research Article Modeling and Visualization of Human Activities for Multicamera Networks" potx
... −1 0] and D2 = [Iτ −1 0; 0] These estimates of C and A constitute the model parameters for each action segment For the case of flow, the same estimation procedure is repeated for the x- and y-components ... the indexed information Visualization and Rendering The visualization subsystem is responsible for synthesizing the output of all of the other subsystems and algorithms and transforming them into ... activities performed by the humans Finally, Section describes the modeling, rendering, and animation of virtual actors for visualization of the sensed data EURASIP Journal on Image and Video Processing...
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Báo cáo hóa học: " Research Article Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms" doc
... reported, for example, see [2–12] and references therein The circuits diagram and connection pattern implementing for the delayed BAM neural networks can be found in [8] Most widely studied and used neural ... Solitons and Fractals, vol 29, no 2, pp 446–453, 2006 [13] Z.-H Guan and G Chen, “On delayed impulsive Hopfield neural networks, ” Neural Networks, vol 12, no 2, pp 273–280, 1999 [14] Z Yang and D ... recurrent neural networks with time delays,” IEEE Transactions on Circuits and Systems I, vol 52, no 5, pp 920–931, 2005 [28] Q Zhang, X Wei, and J Xu, “New stability conditions for neural networks...
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Báo cáo hóa học: " Research Article Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography" docx
... part of the data for training artificial neural networks (500 BCG cycles used for MLP nets and 300 BCG for RBF nets) and the rest of the data (2000 BCG cycles) for testing the performance of the ... data for training and testing the system On the other hand, in this study there were no excluded subjects for testing and we used the same subjects for both training and testing the MLP and RBF neural ... weights, and stigma RESULTS To demonstrate the performance of our approaches and to compare results, we used MLP (two hidden layers with 15 and 10 neurons relatively) and RBF neural networks...
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Báo cáo hóa học: " Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach" pptx
... frequency, are reported For the trials, a power class three [17] for Bluetooth and a 25 mW power level for WLAN are considered [16] Bit rate equal to Mbps for Bluetooth and 11 Mbps for IEEE 802.11b ... Van Dyck, and A Soltanian, “Interference of bluetooth and IEEE 802.11: simulation modeling and performance evaluation,” in Proc 4th International ACM Workshop on Modeling, Analysis and Simulation ... distribution [13] and the CW distribution [27] Both have advantages and disadvantages as explained below The WV distribution is the prototype for all TF transforms, and is the most widely used and the...
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Flexibility and accuracy enhancement techniques for neural networks
... column stand for the numbers of hidden units for the new sub -networks in IOL-1 and numbers of hidden units for the overall structures in retraining The number of hidden units for the old sub -networks ... column stand for the numbers of hidden units for the new sub -networks in IOL-2 and numbers of hidden units for the overall structures in retraining The Number of hidden units for the old sub -networks ... easy for engineers to obtain new ideas from biological brain to develop neural network for complex problems Because of the useful properties, neural networks are more and more widely adopted for...
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Predictive Modeling Of Surface Roughness And Tool Wear In Hard Turning Using Regression And Neural Networks
... necessary for tool wear measurement therefore necessary information should be extracted from them The information can be used for either modeling the relation between cutting process variables and ... feedforward neural network with hyperbolic tangent-sigmoid transfer functions performed better among feed-forward ¨ zel and Nadgir [26] developed a neural network models O back-propagation neural ... errors for second group of neural networks Neural network Average rms error Tool flank wear prediction for honed tools Tool flank wear prediction for chamfered tools Surface roughness prediction for...
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Neural Networks (and more!)
... FIGURE 26-11 Neural network performance These are histograms of the neural network's output values, (a) for the training data, and (b) for the remaining images The neural network performs better ... biological and nonbiological systems Chapter 26- Neural Networks (and more!) 459 Neural network research is motivated by two desires: to obtain a better understanding of the human brain, and to ... of information For instance, there might be outputs for: submarine (yes/no), whale (yes/no), undersea mountain (yes/no), etc Chapter 26- Neural Networks (and more!) 461 x1 x2 FIGURE 26-6 Neural...
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ISOTOPE HYDROGRAPH SEPARATION FOR MODELING OF RUNOFF MECHANISMS OF ATMOSPHERICALLY DERIVED CHEMICAL AND RADIOACTIVE POLLUTANTS
... Prefecture and the Ministry of Land, Infrastructure and Transport are appreciated The authors thank Dr Mariko Atarashi-Andoh for her aid in mass spectrometry We also thank Mr Takashi Ueno and Mr ... appropriate for the analysis Mixed standards (XSTC-1, -7 , -8 and -13, SPEX) were used for calibration The concentration of Si was determined by spectrophotometry using ammonium molybdate RESULTS AND ... with a gas chromatography column for separation of CO2 The standard deviation of our measurements was in the range of ±0.1 to ±0.3 ‰ and ±0.2 to ±0.4 ‰ for hydrogen and oxygen, respectively Inorganic...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc
... components: A forward filter in the form of a Kalman filter A backward filter in the form of an information filter A separate smoother, which combines results embodied in the forward and backward ... k and Hk is the measurement matrix The measurement noise vk is assumed to be additive, white, and Gaussian, with zero mean and with covariance matrix defined by E½vn vT k & ¼ Rk for n ¼ k; for ... scalar random variables; generalization of the theory to vector random variables is a straightforward matter Suppose we are given the observable yk ¼ xk þ vk ; where xk is an unknown signal and vk...
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