29035045
Genetic algorithms for determining fuzzy measures from data
A synthetic evaluation of a given object in terms of multiple factors that contribute to some feature of the object (quality, performance, etc.) may be regarded as a system with multiple inputs and one output. Traditionally, the output is expressed as the weighted average of the inputs. Unfortunately, this method is severely limited as it cannot capture any inherent relation among the factors involved. This limitation can be overcome by using the Choquet integral or the fuzzy integral with respect to a fuzzy measure that captures the relation among the factors. The crux of this method is to determine the right fuzzy measure. In this paper, we describe an efficient genetic algorithm for constructing a suitable fuzzy measure from relevant input-output data. This algorithm has a broad applicability in various problem areas, such as decision making, cluster analysis, pattern recognition, image and speech processing, and expert systems.
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.
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.
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.
204983610
Pyramidic Clustering of Large-Scale Microarray Images
With ongoing research and development of imaging techniques such as those involved in brain MRIs, cDNA microarrays and satellite reconnaissance, the need for tools that can intelligently parse larger images is ever increasing. One group of such techniques often used is that of segmentation, an example of which is that of clustering algorithms. In order to deal with large data sets, current approaches require the data to be sampled or summarized before true analysis can take place. In this paper we propose a novel image analysis technique using pyramidic type grouping, namely copasetic clustering, which focuses on the problem of applying traditional clustering techniques to these large-scale image data sets with limited resources. A further benefit of the technique is the transparency of its intermediate clustering steps; when applied to spatial data sets this allows the capture and incorporation of contextual information to improve result accuracy. The algorithm achieves an ∼1--3 dB w-to-noise ratio when compared with the conventional techniques described.
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.
6449041
Adaptive frame prediction for foveation scalable video coding
Embedded rate scalable video coding allows for the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. This is a very attractive feature for many multimedia communication applications. Motion estimation (ME) /motion compensation (MC) techniques are widely employed in various video coding systems to reduce temporal information redundancy. One of the major challenging problems in ME/MC based rate scalable video coding is how to generate the prediction frame from the previous frame to match the current frame. This problem is more difficult in rate scalable coding than in fixed rate coding because the decoding data rate is unavailable to the encoder. We propose an adaptive frame prediction scheme for foveation scalable video coding (FSVC), which is a new video coding algorithm that combines a foveation-based human visual system (HVS) model with a wavelet-based rate scalable coding algorithm. The new frame prediction algorithm provides an adaptive mechanism to control the prediction errors while reduce error propagation.
47404061
Diversity and Multiplexing Tradeoff in General Fading Channels
The optimal tradeoff between diversity gain and multiplexing gain for multiple-inputmultiple-output (MIMO) channels has been studied recently under the independent and identically distributed (i.i.d.) Rayleigh-fading assumption. In this correspondence, this result is extended and the optimal tradeoff performance is derived for generalized fading channel conditions, including different fading types, nonidentical fading distributions, spatial correlation, and nonzero channel means. Our results include many known models as special cases and shed light on the effects of different channel parameters on the optimal tradeoff performance
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.