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In granular computing based on rough set, the equivalent relation in rough set theory can be expressed by equivalent granule. In this paper, authors developed binary granule encoding algorithm of decision information system (BGrE-DIS) and core attributes acquisition algorithm under the binary granule expression (CAA-BGrE). Furthermore, a fundamental model of binary granule neural network was established. The proposed binary granule neural network was valuated by a fault diagnosis simulation example given in the end of this paper to prove the validity of the proposed model and rapidity of these proposed algorithms. [ABSTRACT FROM AUTHOR]

On the basis of analyzing on the comparability of the recognizing principle between antigens and antibodies, mathematical model and Singular Value Decomposition (SVD) of matrix, an approach to fault diagnosis combining Principal Component Analysis (PCA) with Artificial Immune is proposed. PCA is used to abstract the characteristic and reduce the dimensionality of the data. SVD is used to get the pairs of antibody and antigen of data to diagnose faults. The data from Tennessee Eastman (TE) process simulator is used to evaluate the effectiveness of this approach. Simulation result shows that the average fault diagnosis ratio for TE process can be up to 84% and this approach is practicable. [ABSTRACT FROM AUTHOR]

This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), numbers of graduate students, and topics being investigated. Statistical summaries showing the growth in the research on rough set theory and application in China are included. In addition, an introduction summarizing the research interests of Chinese researchers is included in this article. The contribution of this article is a complete overview of the principal research directions in rough set theory and its applications in China. [ABSTRACT FROM AUTHOR]

In this paper, Rough Set Theory (RST) was introduced to discover knowledge hidden in the evolution process of Genetic Algorithm. Firstly it was used to analyze correlation between individual variables and their fitness function. Secondly, eigenvector was defined to judge the characteristic of the problem. And then the knowledge discovered was used to select evolution subspace and to realize knowledge-based evolution. Experiment results have shown that the proposed method has higher searching efficiency, faster convergent speed, and good performance for deceptive problem and multi-modal problems. [ABSTRACT FROM AUTHOR]

A new realtime interpolation algorithm for color image is presented. The algorithm is based on the concept of indiscernibility relation in rough sets (RS) theory. By applying the concept of upper and lower approximation based on the continuity of images, the image is first divided into homogenous area, edge pixels and isolated pixels. Then ]> surface interpolation is further achieved using the information of classification. Besides emulation, the technology has been applied to the visual presenter with low-resolution image sensor. Results demonstrate that the new algorithm improves substantially the subjective and objective quality of the interpolated images over conventional interpolation algorithms, and meets the requirements of real time image processing. The algorithm represents an attempt to incorporate RS in image processing. [ABSTRACT FROM AUTHOR]

When samples number, classification and dimension of clustering are much more, traditional clustering algorithm usually leads to unharmonious character between clustering and transcendent knowledge. Therefore, a new clustering algorithm is proposed, which is parallel artificial immune clustering algorithm based on granular computing. Artificial immune system model has the characteristics, such as parallel, random searching and maintaining diversity, which can solve premature problem in latter evolution and converge to a global optimization solution faster. Besides, we unite it to dynamic granulation model and apply granulation description to clustering. In the process of granulation changing, we can choose appropriate granulation size by adjusting to ensure clustering efficiency and quality. Tests show that the algorithm is more effective and more reasonable when we handle clustering of some data with it. [ABSTRACT FROM AUTHOR]

The recommendation G.728 depends on the Levinson-Durbin (L-D) algorithm to update its gain filter coefficients. In this paper, it is contrasted with BP neural network method. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. This paper proposes a scheme to estimate SNR so that gain predictor can be separately optimized with quantizer. Using BP neural network filter, the calculation quantity is only 6.7 percent of L-D method's and its average segment SNR is about 0.156dB higher than G.728. It is also used to evaluate the case that excitation vector is 16 or 20 samples, respectively, the BP neural network algorithm has similarly good result. [ABSTRACT FROM AUTHOR]

Choice of ADPCM's step-size updating factors M has a sea capacity of computing that optimizes multi-variables with complex target function. There is the effective scheme such as GA or MEA but its convergence rate becomes too slowly in the neighborhood of peak value of multi-peak function to come away from local optimization. The Clone Mind Evolution Algorithm (CMEA) that introduces the clone operator to reserve the strong component of the weak individual to next iterativeness, which effect is very obvious with testing the typical function, comes into the MEA's similartaxis operator and is used to optimize ADPCM's 8 step-size updating factors. The experiment result shows that the CMEA's SNR has been reformed average 1.03dB every generation, which is exceeding MEA's by 0.4dB, in beginning five of iterativeness and overrun the MEA's from generation 5. Furthermore, the MEA's quantity of computing is equal to CMEA's by 1.67 times and the latter is of anti-prematurely. [ABSTRACT FROM AUTHOR]

In this paper, Immune Mind Evolutionary Algorithm (IMEA) is introduced to correct the nonlinearity of frequency modulation (FM) of voltage controlled oscillator (VCO) in linear frequency modulation continuous wave (LFMCW) radar level gauge. Firstly, the FM voltage is divided into several subsections, then by using fast Fourier transform (FFT) for the beat frequency signals, the characteristic of the spectrum is distilled, and furthermore an evaluation function is constructed. IMEA is applied to optimize the endpoint coordinates of the subsections to get nonlinear curve of FM voltage so as to compensate for the nonlinearity of VCO. Experiments show that the proposed method has good correction performance with no requirement for additional hardware, and it can complete correction in a relative short time. [ABSTRACT FROM AUTHOR]

A new algorithm of evolutionary computing, which combines clone selective algorithm involved in artificial immunity system theory and mind evolution algorithm (MEA) proposed in reference [4], is presented in this paper. Based on similartaxis which is the one of MEA operators, some operators borne by the new algorithm including such as clone mutation, clone crossover, clone selection are also introduced. Then the clone mind evolution algorithm (CMEA) is developed by using the diversity principle of antigen-antibody. Not only can CMEA converge to globally optimal solution, but also it solve premature convergence problem efficiently. The simulating results of the representative evaluation function show that the problem of degeneration phenomenon existing in GA and MEA can be perfectly solved, and the rapidity of convergence is evidently improved by CMEA studied in the paper. In the example of the solution to the numerical problem, the search range of solution is expanded and the possibility of finding the optimal solution is increased. [ABSTRACT FROM AUTHOR]

Coal type has great influence on the measurement of the unburned carbon in fly ash in thermal power plant. Based on analyzing the factors mainly affect the measurement accuracy, microwave technique and capacitance technology combined with the specific resistivity of fly ash are adopt to construct the measuring system based on the multi-sensor fusion. Then the fuzzy technology is applied to decrease the temperature influence on the measuring result of ash resistivity, and BP neural network is utilized to fuse the multi-sensor information effectively. Experiment results shown that the proposed method can effectively decrease the coal type effect on the unburned carbon measurement, however at the same time the accuracy for the same coal type is increased. [ABSTRACT FROM AUTHOR]