1885677
Learning nonlinear multiregression networks based on evolutionary computation
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
8746593
Modal logic interpretation of Dempster-Shafer theory: An infinite case
Abstract A modal logic interpretation of belief and plausibility measures defined on infinite sets is established. As a special case, a modal logic interpretation of necessity and possibility measures defined on infinite sets is also established. It is proven in both cases that the interpretation is complete. These results establish, in effect, modal logic interpretations of the Dempster-Shafer theory and possibility theory.
26197775
Using Neural Networks to Determine Sugeno Measures by Statistics
Abstract To replace the traditional weighted average method, Choquet integrals or Sugeno integrals with respect to fuzzy measures are used to obtain a synthetic evaluation of a given object (or its quality, function, etc. respectively) with multi-attribute. Generally, it is not easy to determine fuzzy measures in real problems due to the subjectivity of human thinking. It is even much more difficult than determining weights in the weighted average method, because of the nonadditivity of fuzzy measures. This paper uses a neural network algorithm to optimize the inverse problem of synthetic evaluation, and thus to determine Sugeno measures by the Choquet integral and statistics of given data. Since the Choquet integral is a generalization of the weighted average method, this technology has a broad applicability in areas of multivariate analysis, decision making, pattern recognition, image and speech processing and expert systems. Copyright © 1996 Elsevier Science Ltd.
5251967
Foveation scalable video coding with automatic fixation selection
Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.
33403859
Area-power-time efficient pipeline-interleaved architectures for wave digital filters
Various forms of pipelining are explored for low power implementation of lattice wave digital filters realized with 3-port adaptors. In these filters, the time performance of pipelining is constrained by recursion. Using the fastest, block pipelined architecture as a reference point, it is shown that additional levels of pipelining can be applied to reduce the power consumption, at the expense of slightly changing the maximum sample rate. In one case power is reduced by 65% with only a modest speed penalty. Area increases due to additional pipeline registers can be more than offset if the consequent interleaving capability is utilized.
30635259
On global exponential stability of generalized stochastic neural networks with mixed time-delays
Abstract This paper is concerned with the global exponential stability analysis problem for a general class of stochastic neural networks with mixed time-delays. The mixed time-delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed stochastic neural network is exponentially stable in the mean square in the presence of both the discrete and distributed delays. By employing a new Lyapunov–Krasovskii functional and conducting stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria of the exponential stability. Furthermore, the main results are specialized to deal with the analysis problem for the global asymptotic stability within the same LMI framework. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.
26353125
On the better protection of short-frame turbo codes
Protecting short data frames by turbo coding is a challenging task because of the small interleaver size and the need for transmission efficiency. In this letter, turbo-decoding-metrics aided short cyclic redundancy check codes are applied to novel tailbiting encoded trellis codes with a twofold purpose: to stop the iterative decoding processes to achieve low-power design and to reduce fractional coding-rate loss. Significant coding gains can be achieved by actually increasing the transmission rate with a negligible increase in power consumption. Performance improvement is demonstrated over additive white Gaussian noise channels. The savings is up to 21.4% for the transmission throughput and 21.5% for the energy consumption of the turbo decoder when frame size 49 is used.
12482658
On circulant chromatic number and circulant chromatic function
Abstract A new concept of circulant chromatic function of a graph is introduced to generalize the concept of chromatic polynomial of a graph. This is similar to the generalization from the concept of chromatic number to the concept of circulant chromatic number. We provide circulant chromatic functions for a few classes of graphs. We consider circulant chromatic functions on the graphs obtained by elementary operations on given graphs, and the relationship between the circulant chromatic number of a graph and that of its complement. We also investigate some sufficient conditions for the circulant chromatic number and the chromatic number of a graph to be equal to each other.