藏族的“鲁”文化探析

文章从藏族崇拜"鲁"神的起源,对"鲁"神的认识,崇拜"鲁"神的方式及崇拜的原因等四方面入手,对藏族"鲁"神文化进行了分析研究。作者认为,"鲁"是一种古老苯教文化的遗留,在其发展过程中,产生了形式多样的"鲁"神崇拜、祭祀和禁忌习俗,研究这一文化现象对现实生活也具有一定的借鉴意义。

ADAPTIVE RECONFIGURATION CONTROL FOR FIGHTERS BASED ON WEIGHTED MULTIPLE-MODEL-STRUCTURE

Aimed at the complex flight control system of a fighter,a kind of robust adaptive control methods using multiple models is presented to make the control system track the given signal under different working conditions and to reconfigure the control law for some structural failures. Firstly,the multiple-model control structure is formed by several linear models and one fuzzy model. In the fuzzy logic way,weights of the multiple-model adaptive controller are obtained. Then,a dynamic structure adaptive neural network is introduced to stabilize the whole system and eliminate the influence caused by the frequent switching. Simulation results show that the control method is effective by demonstrating the normal flight process and the control simulation with failures.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

Robust multi-layer extreme learning machine using bias-variance tradeoff

As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ability.However,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured problems.To resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ability.Moreover,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training data.The generalization ability of the original ELM decreases under such circumstances.To address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex datasets.Simulation results show that the method has high generalization ability and strong robustness to noise.

Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

Event-related potentials associated with Chinese and English color-word Stroop tasks in Chinese bilinguals

Objective:To investigate the neural electrophysiologieal activity underlying Chinese and Eng- lish Stroop tasks for Chinese English bilinguals.Methods:Event-related potentials(ERPs)were recorded in 14 Chinese bilinguals with a moderate command of English when they performed the Stroop task pre- sented in English words and Chinese characters,respectively.Results:In Chinese task version,it was found an increased positivity over bilateral front-polar regions on incongruent trials compared with congru- ent trials,followed by an increased negativity over fronto-central region and an increased positivity over occipital region.While in English task version,only the increased negativity was observed over fronto-cen- tral region,but with reduced amplitude and anterior distribution.Conclusion:This increased negativity was proposed as an index of the resolution processes of conflicting information in the incongruent situa- tion.The increased positivity over occipital region on Chinese incongruent trials may indicate visually rechecking effect for Chinese character.

Robustness Design for CNN Templates with Performance of Extracting Closed Domain

The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.

Seventy Cases of External Humeral Epicondylitis Treated by Local Blocking and Massotherapy

External humeral epicondylitis or tennis elbow, is a commonly encountered disease in orthopaedics and traumatology. The curative effect is not satisfactory as far as its treatment by western and traditional Chinese medicine is concerned, which has a long course of treatment and a high recurrent rate. 70 cases of external humeral epicondylitis were treated by massotherapy after local blocking from August 1995 to October 1997 at this hospital with satisfactory therapeutic effects.Clinical DataOf the 70 cases in this series treated by massotherapy after local blocking, 30 were males and 40 females, ranging in age from 19 to 65 years. 55 cases were 30 to 50 years old, 20 had the left elbow affected and 50 the right elbow. 7 cases had a history of trauma, 50 a history of chronic strain, and 13 the cause unknown. The shortest duration of disease was 15 days and the longest 24 months.Among 50 cases in the control group treated by massotherapy, 20 cases were males and 30 females, ranging in age from 16 to 58 years. 40 cases were 30 to 50 years old. 15 cases were affected on the left elbow and 35 on the right elbow. 6 cases had a history of trauma, 32 a history of chronic strain, and 12 the cause unknown. The shortest duration of diseases was 7 days and the longest 22 months.

Robustness, Death of Spiral Wave in the Network of Neurons under Partial Ion Channel Block

The development of spiral wave in a two-dimensional square array due to partial ion channel block （Potas- sium, Sodium） is investigated, the dynamics of the node is described by Hodgkin-Huxley neuron and these neurons are coupled with nearest neig1 bor connection. The parameter ratio xNa （and xK）, which defines the ratio of working ion channel number of sodium （potassium） to the total ion channel number of sodium （and potassium）, is used to measure the shift conductance induced by channel block. The distribution of statistical variable R in the two-parameter phase space （parameter ratio vs. poisoning area） is extensively calculated to mark the parameter region for transition of spiral wave induced by partial ion channel block, the area with smaller factors of synchronization R is associated the parameter region that spiral wave keeps Mive and robust to the channel poisoning. SpirM wave keeps alive when the poisoned area （potassium or sodium） and degree of intoxication are small, distinct transition （death, several spiral waves coexist or multi-axm spiral wave emergence） occurs under moderate ratio XNa （and XK） when the size of blocked area exceeds certain thresholds. Breakup of spiral wave occurs and multi-axm of spiral waves are observed when the channel noise is considered.