When the traditional decision tree algorithm is applied to the ordinal classification task, there are two problems:the traditional decision tree algorithm does not introduce the order relation, so it cannot learn and extract the order structure of the dataset; in real life, there is a lot of fuzzy but not exact knowledge, however the traditional decision tree algorithm cannot deal with the data with fuzzy attribute value. To solve these problems, an ordinal decision tree algorithm based on fuzzy advantage complementary mutual information was proposed. Firstly, the dominant set was used to represent the order relations in the data, and the fuzzy set was introduced to calculate the dominant set for forming a fuzzy dominant set. The fuzzy dominant set was able to not only reflect the order information in the data, but also obtain the inaccurate knowledge automatically. Then, the complementary mutual information was generalized on the basis of fuzzy dominant set, and the fuzzy advantage complementary mutual information was proposed. Finally, the fuzzy advantage complementary mutual information was used as a heuristic method, and an decision tree algorithm based on fuzzy advantage complementary mutual information was designed. Experimental results on 5 synthetic datasets and 9 real datasets show that, the proposed algorithm has less classification errors compared with the classical decision tree algorithm on the ordinal classification tasks.

A Minimum Mean Square Error (MMSE) speech enhancement algorithm based on Laplacian distribution was proposed to solve the problem of noise residual and speech distortion of speech enhanced by the spectral subtraction algorithm based on Gaussian distribution. Firstly, the original noisy speech signal was framed and windowed, and the Fourier transform was performed on the signal of each processed frame to obtain the Discrete-time Fourier Transform (DFT) coefficient of short-term speech. Secondly, the noisy frame detection was performed to update the noise estimation by calculating the logarithmic spectrum energy and spectral flatness of each frame. Thirdly, based on the assumption of Laplace distribution of speech DFT coefficient, the optimal spectral subtraction coefficient was derived under the MMSE criterion, and the spectral subtraction with the obtained coefficient was performed to obtain the enhanced signal spectrum. Finally, the enhanced signal spectrum was subjected to inverse Fourier transform and framing to obtain the enhanced speech. The experimental results show that the Signal-to-Noise Ratio (SNR) of the speech enhanced by the proposed algorithm is increased by 4.3 dB on average, and has 2 dB improvement compared with that of the speech enhanced by the over-subtraction method. In the term of Perceptual Evaluation of Speech Quality (PESQ) score, compared with that of over-subtraction method, the average score of the proposed algorithm has a 10% improvement. The proposed algorithm has better noise suppression and less speech distortion, and has a significant improvement in SNR and PESQ evaluation standards.

In Dynamically Tuned Gyroscope (DTG) system, traditional identification methods, including least square identification method and traditional frequency domain identification method, could not achieve acceptable identification fitness degree. To deal with this problem, outlier-eliminated frequency identification method was proposed. In consideration of the characteristics of DTG model structure and intrinsic colored noise, outlier-eliminated method was applied to DTG frequency domain identification. The experimental results indicate that outlier-eliminated frequency identification method, with a fitness degree above 90%, compared with both least square identification method and traditional frequency domain identification method, has a better performance. In addition, outlier-eliminated frequency identification method possesses of good repeatability and stability. Outlier-eliminated frequency identification method could improve the identification fitness degree of DTG system.