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This paper gives a new method of rough set-based on taste signals identification. Further improve the identification accuracy by dividing regions more appropriate. The simulation data and the latest UCI machine learning taste signal data (Wine Quality) are used to verify the new method, and the new method is compared with other identification algorithms. The results fully show the correctness and effectiveness of the proposed identification method based on rough set.

In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic algorithms, such as climb hill algorithm, simulation anneal algorithm and taboo search algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs.

In order to protect the copyright of digital productions, a novel watermarking algorithm based on phase congruency corner detection and Singular Value Decomposition (SVD) is presented. By introducing the phase congruency detects corners and edges method, the attacked watermarked image can be corrected and the watermark synchronization is realized. In this way, which are used for information hiding, can be correctly detected even after signal processing and geometric attacks. The characteristic of U matrix in the result of SVD is finally applied for watermark embedding. The application of the proposed method makes the watermark capable of transparent hiding. Simulation results show that our method proved to be extremely robust against different attacks.

Least squares support vector machines (LSSVM) has been carried out in order to obtain a statistically meaningful analysis of the extended set of molecules. The combined HF with LSSVM correction approach ???LSSVM/HF??? has been applied to evaluate the transition energies of organic molecules. After LSSVM correction, the RMS deviations of the calculated transition energies reduce from 0.91 to 0.26 eV for HF methods. And, this LSSVM/HF is a excellent method to predict transition energies and extend the reliably and efficiently of calculated transition energies.

Least squares support vector machines (LS-SVM) was introduced to improve the calculation accuracy of low level density functional theory. As a demonstration, this combined low level quantum mechanical calculation with LS-SVM correction approach has been applied to evaluate the absorption energies of 160 organic molecules. After LS-SVM correction, the RMS deviations of the calculated absorption energies reduce from 0.95 to 0.16 eV for B3LYP/STO-3G methods. And, this LS-SVM/B3LYP/STO-3G is a excellent method to predict absorption energies and can be used as an alternative to experimental measurements and high-accurate quantum chemical calculations to obtain the value of absorption energies reliably and efficiently.