Pattern memory analysis of second-order neural networks with time-delay

In this paper, the pattern memory of the second-order neural networks with time-delay are investigated based on the stability theory. Several sufficient conditions are obtained such that the equilibrium point is locally exponential stable when the equilibrium point located in the saturation region. These conditions can be directly derived from the synaptic connection weights and the external input of the second-order neural networks, and be verified easily. In addition, four examples are given to show the effectiveness of the results.     

绘系统状态轨线的一种方法

介绍了一种作系统运动积分曲线的方法，并运用该方法绘出几个常见动力学系统的状态轨线，说明其实用价值。     

Scheduling electric power production at a wind farm

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon selected by a decision maker. Different operational scenarios are constructed based on time series data of electricity price, grid demand, and wind speed. The computational results provide insights into management of a wind farm.     

Evaluation of fuzzy regression models by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Improvements to message computation in lazy propagation

Even though existing algorithms for belief update in Bayesian networks (BNs) have exponential time and space complexity, belief update in many real-world BNs is feasible. However, in some cases the efficiency of belief update may be insufficient. In such cases minor improvements in efficiency may be important or even necessary to make a task tractable. This paper introduces two improvements to the message computation in Lazy propagation (LP): (1) we introduce myopic methods for sorting the operations involved in a variable elimination using arc-reversal and (2) extend LP with the any-space property. The performance impacts of the methods are assessed empirically.     

Directed transport and complex dynamics of vortices in a Josephson junction network driven by modulated currents

Directed transport of vortices in a Josephson junction network (JJN) with structural disorder is studied using a numerical simulation. Using spatiotemporal modulation of driving currents, the directed transport of vortices occurs even in the presence of disorder under certain conditions. From the analyses of the current–voltage and local voltage characteristics, it is found that the collective motion of vortices has an importance for the occurrence of directed transport.     

Polypeptide folding on a conformational‐space network: Dependence of network topology on the structural discretization procedure

Mapping the conformational space of a polypeptide onto a network of conformational states involves a number of subjective choices, mostly in relation to the definition of conformation and its discrete nature in a network framework. Here, we evaluate the robustness of the topology of conformational‐space networks derived from Molecular Dynamics (MD) simulations with respect to the use of different discretization (clustering) methods, variation of their parameters, simulation length and analysis time‐step, and removing high‐frequency motions from the coordinate trajectories. In addition, we investigate the extent to which polypeptide dynamics can be reproduced on the resulting networks when assuming Markovian behavior. The analysis is based on eight 500 ns and eight 400 ns MD simulations in explicit water of two 10‐residue peptides. Three clustering algorithms were used, two of them based on the pair‐wise root‐mean‐square difference between structures and one on dihedral‐angle patterns. A short characteristic path length and a power‐law behavior of the probability distribution of the node degree are obtained irrespective of the clustering method or the value of any of the tested parameters. The average cliquishness is consistently one or two orders of magnitude larger than that of a random realization of a network of corresponding size and connectivity. The cliquishness as function of node degree and the kinetic properties of the networks are found to be most dependent on clustering method and/or parameters. Although Markovian simulations on the networks reproduce cluster populations accurately, their kinetic properties most often differ from those observed in the MD simulations. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Exclusively Heteronuclear NMR Experiments to Obtain Structural and Dynamic Information on Proteins

Provided that ¹³C‐detected NMR experiments are either preferable or complementary to ¹H detection, we report here tools to determine C^α? C′, C′? N, and C^α? H^α residual dipolar couplings on the basis of the CON experiment. The coupling constants determined on ubiquitin are consistent with the subset measured with the ¹H‐detected HNCO sequences. Since the utilization of residual dipolar couplings may depend on the mobility of the involved nuclei, we also provide tools to measure longitudinal and transverse relaxation rates of N and C′. This new set of experiments is a further development of a whole strategy based on ¹³C direct‐detection NMR spectroscopy for the study of biological macromolecules.