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The global asymptotic stability for Hopfield neural networks with time delay was investigated. A theorem and two corollaries were obtained, in which the boundedness and differentiability of fj on R in some articles were deleted. Some sufficient conditions for the existence of global asymptotic stable equilibrium of the networks in this paper are better than the sufficient conditions in quoted articles.

Fracturing operations can effectively improve the production of low-permeable reservoirs. The performance of fracturing fluids directly affects the fracturing efficiency and back flow capacity. As polymer-based fracturing fluids (such as guar gum (GG), polyacrylamide (HPAM), etc.) are high-viscosity fluids formed by viscosifiers and crosslinking agents, the degree of gel breakage after the fracturing operation directly influences the damage degree to the reservoir matrix and the mobility of oil angd gas produced from the reservoir into the wellbore. This study compared the viscosity, molecular weight, and particle size of the fracturing fluid after gel breakage prepared by GG and HPAM as viscosifiers, as well as evaluate their damage to the core. Results show that the viscosities of the gel-breaking fluid increased with the concentration of the viscosifier for both the HPAM-based and GG-based fracturing fluids. For the breaking fluid with the same viscosity, the molecular weight in the HPAM-based gel-breaking fluid was much larger than that in the GG-based system. Moreover, for the gel-breaking fluid with the same viscosity, the molecular particle size of the residual polymers in the HPAM-based system was smaller than that in the GG-based system. The damage to the core with the permeability of 1 × 10−3 μm2 caused by both the HPAM-based and GG-based gel-breaking fluids decreased with the increase in the solution viscosity. For the gel-breaking fluid systems with the same viscosity (i.e., 2–4 mPa s), the damage of HPAM-based fracturing fluid to low-permeability cores was greater than the GG-based fracturing fluid (45.6%–80.2%) since it had a smaller molecular particle size, ranging from 66.2% to 77.0%. This paper proposed that the damage caused by hydraulic fracturing in rock cores was related to the partilce size of residual polymers in gel-breaking solution, rather than its molecular weight. It was helpful for screening and optimizing viscosifiers used in hydraulic fracturing process.

Feedback is a significant strategy for the control of quantum system. Information acquisition is the greatest difficulty in quantum feedback applications. After discussing several basic methods for information acquisition, we review three kinds of quantum feedback control strategies: quantum feedback control with measurement, coherent quantum feedback, and quantum feedback control based on cloning and recognition. The first feedback strategy can effectively acquire information, but it destroys the coherence in feedback loop. On the contrary, coherent quantum feedback does not destroy the coherence, but the capability of information acquisition is limited. However, the third feedback scheme gives a compromise between information acquisition and measurement disturbance.

Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model, under the weaker assumptions on nonlinear activation functions, are concerned. By constructing suitable Liapunov function, some simpler criteria for global attractivity and global exponential stability for Hopfield continuous neural networks with time delays are presented.

As a widely used reinforcement learning method, Q-learning is bedeviled by the curse of dimensionality： The computational complexity grows dramatically with the size of state-action space. To combat this difficulty, an integrated hierarchical Q-learning framework is proposed based on the hybrid Markov decision process （MDP） using temporal abstraction instead of the simple MDP. The learning process is naturally organized into multiple levels of learning, e.g., quantitative （lower） level and qualitative （upper） level, which are modeled as MDP and semi-MDP （SMDP）, respectively. This hierarchical control architecture constitutes a hybrid MDP as the model of hierarchical Q-learning, which bridges the two levels of learning. The proposed hierarchical Q-learning can scale up very well and speed up learning with the upper level learning process. Hence this approach is an effective integral learning and control scheme for complex problems. Several experiments are carried out using a puzzle problem in a gridworld environment and a navigation control problem for a mobile robot. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model, under the weaker assumptions on nonlinear activation functions, are concerned. By constructing suitable Liapunov function, some simpler criteria for global attractivity and global exponential stability for Hopfield continuous neural networks with time delays are presented.

A learning algorithm based on state superposition principle is presented. The physical implementation analysis and simulated experiment results show that quantum mechanics can give helps in learning for more intelligent robot.