12985850
Learning a class of large finite state machines with a recurrent neural network
Abstract One of the issues in any learning model is how it scales with problem size. The problem of learning finite state machine (FSMs) from examples with recurrent neural networks has been extensively explored. However, these results are somewhat disappointing in the sense that the machines that can be learned are too small to be competitive with existing grammatical inference algorithms. We show that a type of recurrent neural network (Narendra & Parthasarathy, 1990, IEEE Trans. Neural Networks, 1, 4–27) which has feedback but no hidden state neurons can learn a special type of FSM called a finite memory machine (FMM) under certain constraints. These machines have a large number of states (simulations are for 256 and 512 state FMMs) but have minimal order, relatively small depth and little logic when the FMM is implemented as a sequential machine.
26234971
A Color Consistency Algorithm Between Different Printers
Color consistency between different color peripherals su as printers, scanners and digital cameras is a well-kno problem. In this paper, a color consistency algorith between different printers is proposed. Each printer has its own characteristics. The co behavior of a printer can be modeled by a nonline characteristic function. We approximate the characteris function by a three-dimensional look-up table. Then, transfer function between two printers can be construc based on their characteristic functions. The transfer funct is to compensate one’s color behavior and simula another’s color behavior. After applying the transfe function, the color difference ( ∆E) and hue difference can be reduced to two to three times smaller.
31273280
Adaptive-Hierarchical-Filtering Technique for Image Enhancement
This paper describes a novel adaptive-hierarchical-filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moire pattern during the reproduction of the image by different computer peripherals such as color copier, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moire distortion may result when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moire patterns in the frequency and spatial domain. Based on the analysis, a set of hierarchical filter is developed to suppress the moire artifacts and enhance the image adaptively. The hierarchical filter consists of a set of variable-length low-pass filters and high-pass filters. The low-pass filters have a nice inheritance of canceling aliased low frequency components (moire distortion). High-pass filtering is also applied to sharpen image edges. An image classifier is developed to determine that an edge is either a global true edge (for sharpening enhancement) or a local halftone's micro-structural edge (for LPF for moire reduction), so that adaptive filter technique can be applied to achieve the smooth transition between sharp edges and smooth halftone regions. Thus, we achieves overall high-quality output images.Experimental results have been shown the effectiveness of the presented technique that works well on wide combinations of above-mentioned 6 factors, which will be explained during the following sections, for high-quality magazine image reproduction.
610912
Dynamic search in peer-to-peer networks
This work specifically addresses the search issues in unstructured peer-to-peer (P2P) systems that involve the design of an efficient search algorithm, the proposed dynamic search, and the modeling of P2P systems reflecting real measured P2P networks. Through simulations, we will show dynamic search outperforms other existing ones in terms of performance aspects.
38368452
Multilevel Database and Aggregated Security Algebra
206617892
Granular Computing
The structure and representation theories of (crisp/fuzzy) granulations are presented. The results are applied to data mining, fuzzy control, security and etc.
34363254
Feature Subset Selection Based on Relative Dependency between Attributes
Feature subset selection is an importent component of knowledge discovery and data mining systems to help reduce the data dimensionality. Rough sets theory provides a mechanism of selecting feature subsets. In the rough set community, most feature subset selection algorithms are attributes reduct-oriented; that is, finding minimum reducts of the conditional attributes of a decision table. Two main approaches to finding attribute reducts are categorized as discernibility functions-based and attribute dependency-based. These algorithms, however, suffer from intensive computations of either discernibility functions for the former or positive regions for the latter. In this paper, we propose a new concept, called relative attribute dependency, and present a sufficient and necessary condition of the minimum conditional attributes reduct of a decision table represented with the relative attribute dependency. The relative attribute dependency can be calculated by counting the distinct rows of the sub-decision table, instead of generating discernibility functions or positive regions. Thus the computation efficiency of minimum reducts are highly improved. We develop two algorithms for finding minimum reducts of the conditional attributes, one brute-force algorithm and the other heuristic algorithm using attribute entropy as the heuristic function. We also show the results of these algorithms by an illustrative example.
18065522
The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems
This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic description based on which further control of such systems may be carried out. Simulation results of a second-order parameter varying system demonstrate the effectiveness of the method.