Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Language
Year
  • 1
    Language: English
    In: Advances in Engineering Software, 2010, Vol.41(4), pp.636-646
    Description: This paper proposes a new unsupervised classification approach for automatic analysis of polarimetric synthetic aperture radar (SAR) image. Classification of the information in multi-dimensional polarimetric SAR data space by dynamic clustering is addressed as an optimization problem and two recently proposed techniques based on particle swarm optimization (PSO) are applied to find optimal (number of) clusters in a given input data space, distance metric and a proper validity index function. The first technique, so-called multi-dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem, fractional global best formation (FGBF) technique is then presented, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide” than the PSO’s native particle. In this study, the proposed dynamic clustering process based on MD-PSO and FGBF techniques is applied to automatically classify the color-coded representations of the polarimetric SAR information (i.e. the type of scattering, backscattering power) extracted by means of the Pauli or the Cloude–Pottier decomposition algorithms. The performance of the proposed method is evaluated based on fully polarimetric SAR data of the San Francisco Bay acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at -band. The proposed unsupervised technique determines the number of classes within polarimetric SAR image for optimal classification performance while preserving spatial resolution and textural information in the classified results. Additionally, it is possible to further apply the proposed dynamic clustering technique to higher dimensional ( -D) feature spaces of fully polarimetric SAR data.
    Keywords: Particle Swarm Optimization ; Multi-Dimensional Search ; Dynamic Clustering ; Polarimetric Synthetic Aperture Radar (SAR) ; Engineering ; Applied Sciences ; Computer Science
    ISSN: 0965-9978
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Language: English
    In: Expert Systems With Applications, 2012, Vol.39(5), pp.4710-4717
    Description: In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.
    Keywords: Polarimetric Synthetic Aperture Radar ; Radial Basis Function Network ; Particle Swarm Optimization ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: English
    In: PIERS Online, 2010, Vol.6(5), pp.470-475
    Keywords: Physics;
    ISSN: PIERS Online
    E-ISSN: 1931-7360
    Source: CrossRef
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: Expert Systems With Applications, 2010, Vol.37(12), pp.8450-8461
    Description: In this paper, we investigate the performance of global vs. local techniques applied to the training of neural network classifiers for solving medical diagnosis problems. The presented methodology of the investigation involves systematic and exhaustive evaluation of the classifier performance over a neural network architecture space and with respect to training depth for a particular problem. In this study, the architecture space is defined over feed-forward, fully-connected artificial neural networks (ANNs) which have been widely used in computer-aided decision support systems in medical domain, and for which two popular neural network training methods are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). Both training techniques are compared in terms of classification performance over three medical diagnosis problems ( , and ) from benchmark dataset and computational and architectural analysis are performed for an extensive assessment. The results clearly demonstrate that it is not possible to compare and evaluate the performance of the two algorithms over a single network and with a fixed set of training parameters, as most of the earlier work in this field has been carried out, since training and test classification performances vary significantly and depend directly on the network architecture, the training depth and method used and the available dataset. We, therefore, show that an extensive evaluation method such as the one proposed in this paper is basically needed to obtain a reliable and detailed performance assessment, in that, we can conclude that the PSO algorithm has usually a better generalization ability across the architecture space whereas BP can occasionally provide better training and/or test classification performance for some network configurations. Furthermore, we can in general say that the PSO, as a global training algorithm, is capable of achieving minimum test classification errors regardless of the training depth, i.e. shallow or deep, and its average classification performance shows less variations with respect to network architecture. In terms of computational complexity, BP is in general superior to PSO for the entire architecture space used.
    Keywords: Artificial Neural Networks ; Backpropagation ; Particle Swarm Optimization ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Language: English
    In: Applied Soft Computing Journal, 2011, Vol.11(2), pp.2334-2347
    Description: The need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best ( ) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. In the first approach, is updated (moved) with respect to a global estimation of the gradient of the underlying (error) surface or function and hence can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called , which can replace the native particle for a better guidance, the decision of which is held by a fair competition between the two. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the (not to the entire swarm), both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA parameters are well selected to fit the problem at hand. A major finding of the paper is that even if the SPSA parameters are not tuned well, results of SA-driven (SAD) PSO are still better than the best of PSO and SPSA. Since the problem of poor update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), both approaches are also integrated into MD-PSO and tested over a set of unsupervised data clustering applications. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.
    Keywords: Particle Swarm Optimization ; Stochastic Approximation ; Multi-Dimensional Search ; Gradient Descent ; Computer Science
    ISSN: 1568-4946
    E-ISSN: 18729681
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Language: English
    In: Journal of Biomedical Informatics, June, 2014, Vol.49, p.16(16)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbi.2014.02.005 Byline: Serkan Kiranyaz, Turker Ince, Morteza Zabihi, Dilek Ince Abstract: The illustration of the proposed EEG classification system (top). The illustration of the evolution process of a CNBC (bottom). Display Omitted Article History: Received 29 June 2013; Accepted 3 February 2014
    Keywords: Electroencephalography
    ISSN: 1532-0464
    Source: Cengage Learning, Inc.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    Language: English
    In: Journal of Biomedical Informatics, June 2014, Vol.49, pp.16-31
    Description: The illustration of the proposed EEG classification system (top). The illustration of the evolution process of a CNBC (bottom). This paper presents a novel systematic approach for patient-specific classification of long-term Electroencephalography (EEG). The goal is to extract the seizure sections with a high accuracy to ease the Neurologist’s burden of inspecting such long-term EEG data. We aim to achieve this using the minimum feedback from the Neurologist. To accomplish this, we use the majority of the state-of-the-art features proposed in this domain for evolving a collective network of binary classifiers (CNBC) using multi-dimensional particle swarm optimization (MD PSO). Multiple CNBCs are then used to form a CNBC ensemble (CNBC-E), which aggregates epileptic seizure frames from the classification map of each CNBC in order to maximize the sensitivity rate. Finally, a morphological filter forms the final epileptic segments while filtering out the outliers in the form of classification noise. The proposed system is fully generic, which does not require any information about the patient such as the list of relevant EEG channels. The results of the classification experiments, which are performed over the benchmark CHB-MIT scalp long-term EEG database show that the proposed system can achieve all the aforementioned objectives and exhibits a significantly superior performance compared to several other state-of-the-art methods. Using a limited training dataset that is formed by less than 2 min of seizure and 24 min of non-seizure data on the average taken from the early 25% section of the EEG record of each patient, the proposed system establishes an average sensitivity rate above 89% along with an average specificity rate above 93% over the test set.
    Keywords: EEG Classification ; Seizure Event Detection ; Evolutionary Classifiers ; Morphological Filtering ; Medicine ; Engineering ; Public Health
    ISSN: 1532-0464
    E-ISSN: 1532-0480
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Language: English
    In: International Journal of Clothing Science and Technology, 20 April 2015, Vol.27(2), pp.283-301
    Description: Purpose – The purpose of this paper is to develop an intelligent system for fashion style selection for non-standard female body shapes. Design/methodology/approach – With the goal of creating natural aesthetic relationship between the body shape and the shape of clothing, garments designed for the upper and lower body are combined to fit different female body shapes, which are classified as V, A, H and O-shapes. The proposed intelligent system combines genetic algorithm (GA) with a neural network classifier, which is trained using the particle swarm optimization (PSO). The former, called genetic search, is used to find the optimal design parameters corresponding to a best fit for the desired target, while the task of the latter, called neural classification, is to evaluate fitness (goodness) of each evolved new fashion style. Findings – The experimental results are fashion styling recommendations for the four female body shapes, drawn from 260 possible combinations, based on variations from 15 attributes. These results are considered to be a strong indication of the potential benefits of the application of intelligent systems to fashion styling. Originality/value – The proposed intelligent system combines the effective searching capabilities of two approaches. The first approach uses the GA for identifying best fits to the target shape of the body in the solution space. The second is the PSO for finding optimal (with respect to training mean-squared error) weight and threshold parameters of the neural classifier, which is able to evaluate the fitness of successively evolved fashion styles.
    Keywords: Engineering ; Materials Science ; Particle Swarm Optimization ; Genetic Algorithm ; Fashion Design ; Female Body Shapes ; Neural Networks ; Styling Recommendation ; Engineering ; Economics
    ISSN: 0955-6222
    E-ISSN: 1758-5953
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    Language: English
    In: Neurocomputing, 08 February 2017, Vol.224, pp.142-154
    Description: There are well-known limitations and drawbacks on the performance and robustness of the feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs). In this study we shall address them by Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and in this paper we shall show that this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. Experimental results show that POPs can scale up very well with the problem size and can have the potential to achieve a superior generalization performance on real benchmark problems with a significant gain.
    Keywords: Artificial Neural Networks ; Multi-Layer Perceptrons ; Progressive Operational Perceptrons ; Diversity ; Scalability ; Computer Science
    ISSN: 0925-2312
    E-ISSN: 1872-8286
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages