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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]