2,979 publications from this institution
Computational intelligence techniques have been successfully applied for solving control problems in modern networking architectures such as ATM and the Internet. The introduction of active networks offers a high level of flexibility in customizing the network infrastructure and introducing new functionality. There is a clear need for revisiting both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We elaborate on the characteristics of these technologies, their synergy and report on our study with applying computational intelligence techniques for improved routing on a novel active network resource management architecture.
A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data.
The study is concerned with the fundamentals of granular computing and its use to system modeling and system simulation. In contrast to numerically-driven identification techniques, in granular modeling we concentrate on building meaningful information granules in the space of experimental data and forming the ensuing model as a web of associations between such constructs. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format. First, we elaborate on the role of information granules viewed as basic building modules exploited in model development. Second, we show how information granules are constructed. It is shown how to express relationships (links) between information granules; in this case two measures of linkage are discussed, namely a relevance index and a notion of a fuzzy correlation. Granular computing invokes a number of layers whose existence is implied by different levels of information granularity. We show how to move between these layers by using transformations of encoding and decoding of information granules. Subsequently, some generic architectures of granular modeling are discussed.
The analysis of feature variance is a common approach used for data interpretation. In the case of pattern classification, however, the transformation of correlated features into a new set of uncorrelated variables must be used with caution, as there is no necessary causal connection between discriminatory power and variance. To compensate for this potential shortcoming, we present a classification method that blends variance analysis with an adaptive fuzzy logic network that identifies the most discriminatory set of uncorrelated variables. We empirically evaluate the effectiveness of this method using a suite of biomedical datasets and comparing its performance against two benchmark classifiers.
The human-centric facet of intelligent systems, which are inherently based upon the principle of hybridization of the individual information technologies, is definitely an interesting and highly promising avenue to follow. The diversity of communication channels between information systems and the environment is immense, and their effective realization does call for a truly heterogeneous nature of the underlying technologies and algorithmic frameworks. Consider, for instance, processing visual information, which became omnipresent in many applications including a broad range of inspection and manufacturing systems. Computer vision offered remarkable opportunities of processing therein. Fuzzy sets support a granular view at images and form a setting for their interpretation at the higher, more abstract level. Neural networks and evolutionary optimization provide badly needed learning abilities. The technologies mentioned so far, when combined into an inherently hybrid system, help address the needs of intelligent vision systems and build a coherent and versatile implementation. Managing knowledge at various levels, starting from the detailed pixel-level and moving to the far more general abstract level, becomes crucial to the success of such systems. This special issue is a testimony to the ongoing innovation and recent developments in hybrid information systems. The study by Shahram Jafari and Ray Jarvis focuses on implementing perception in the context of robotic eye-to-hand coordination. The authors show an integration of different novel concepts to perform scene analysis, hand-eye coordination and object manipulation for robotic tasks. The underlying architectural considerations rely on the usage of the neuro-fuzzy systems in the formation of scene analysis and object recognition. Next, Ghosh and Petkov, in their study, deal with the cognitive evaluation of contour based shape descriptors in image processing. In the sequel, first-order logical networks discussed by Kijsirikul and Lerdlamnaochai offer an interesting insight into the problems of logic-based neurocomputing. The proposed method, called First-Order Logical Neural Network (FOLNN), leads to hybrid structures that combine standard feed-forward neural networks and mechanisms of inductive learning. The study by Sehgal, Gondal, and Dooley is focused on an important and timely issue of handling multiple missing values of genetic expression in problems of analysis and classification of microarray data. The authors offer a mechanism of a k-ranked covariance-based missing value imputation and demonstrate its superiority over the k-nearest neighbor method.
Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.
Two models are discussed that integrate heterogeneous fuzzy data of three types: real numbers, real intervals, and real fuzzy sets. The architecture comprises three modules: 1) an encoder that converts the mixed data into a uniform internal representation; 2) a numerical processing core that uses the internal representation to solve a specified task; and 3) a decoder that transforms the internal representation back to an interpretable output format. The core used in this study is fuzzy clustering, but there are many other operations that are facilitated by the models. Two schemes for encoding the data and decoding it after clustering are presented. One method uses possibility and necessity measures for encoding and several variants of a center of gravity defuzzification method for decoding. The second approach uses piecewise linear splines to encode the data and decode the clustering results. Both procedures are illustrated using two small sets of heterogeneous fuzzy data.
