中医经络理论的重新认识

通过对经络的理论研究 ,发现经络理论本身存在着许多原始的认识上的错误 ,如将针灸穴位经络 (狭义经络 )与人体内在一切联系结构 (广义经络 )混同在一起 ,并将针灸穴位经络认为是气血运行的主要通道 ,以及“如环无端”的气血流注学说等。论述广义经络与狭义经络的区别与联系 ,分析形成概念混同的历史原因 ,提出气血流注学说的不切实际 ,指出广义经络与狭义经络各自的内涵 ,重新认识中医的经络理论 ,明确今后临床科研的发展方向。

经络研究与64次诺贝尔奖(下)

传递生命信息三个信使理论的确立,是20世纪生命科学领域最伟大的发现,为探索生命现象的本质与规律及人类疾病的起因铺平了道路,并成为探究经络现象的物质基础。狭义经络的定义,是第一信使和第二信使之间的整个生命信息物质的通道;广义经络的定义,是第一信使、第二信使和第三信使及三个信使体系之间的整个生命信息物质的通道。诺贝尔医学奖已54次向后者颁奖,竟占到颁奖总数的58.06%,诺贝尔化学奖也10次向后者颁奖,竟占到化学奖生物化学学科颁奖总数的48%。广义经络的研究是20世纪后半叶直至本世纪生命科学领域研究的重点和热点。然而对狭义经络体系的研究不仅是广义经络体系研究的难点,还恰为我国学者攻关的重点。探明狭义经络体系的实质和功能,将成我国学者打破诺贝尔医学奖空白记录的突破口。(本文上篇刊登在本刊2006年第11期)

经络研究与64次诺贝尔奖(上)

传递生命信息三个信使理论的确立,是20世纪生命科学领域最伟大的发现,为探索生命现象的本质与规律及人类疾病的起因铺平了道路,并成为探究经络现象的物质基础。狭义经络的定义,是第一信使和第二信使之间的整个生命信息物质的通道;广义经络的定义,是第一信使、第二信使和第三信使及三个信使体系之间的整个生命信息物质的通道。诺贝尔医学奖已54次向后者颁奖,竟占到颁奖总数的58.06%,诺贝尔化学奖也10次向后者颁奖,竟占到化学奖生物化学学科颁奖总数的48%。广义经络的研究是20世纪后半叶直至本世纪生命科学领域研究的重点和热点。然而对狭义经络体系的研究不仅是广义经络体系研究的难点,还恰为我国学者攻关的重点。探明狭义经络体系的实质和功能,将成我国学者打破诺贝尔医学奖空白记录的突破口。

Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

Absolute Exponential Stability of Generalized Dynamical Neural Networks

This paper investigates the absolute exponential stability of generalized neural networks with a general class of partially Lipschitz continuous and monotone increasing activation functions. The main obtained result is that if the interconnection matrix T of the neural system satisfies that - T is an H matrix with nonnegative diagonal elements, then the neural system is absolutely exponentially stable(AEST). The Hopfield network, Cellular neural network and Bidirectional associative memory network are special cases of the network model considered in this paper. So this work gives some improvements to the previous ones.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Reconstruction of Novel Viewpoint Image Using GRNN

A neural statistical approach to the reconstruction of novel viewpoint image us ing general regression neural networks(GRNN) is presented. Different color value will be obtained by watching the same surface point of an object from different viewpoints due to specular reflection, and the difference is related to the pos ition of viewpoint. The relationship between the position of viewpoint and the c olor of image is non linear, neural network is introduced to make curve fitting , where the inputs of neural network are only a few calibrated images with obvio us specular reflection. By training the neural network, network model is obtaine d. By inputing an arbitrary virtual viewpoint to the model, the image of the vir tual viewpoint can be computed. By using the method presented here, novel viewpo int image with photo realistic property can be obtained, especially images with obvious specular reflection can accurately be generated. The method is an image based rendering method, geometric model of the scene and position of lighting are not needed.

Mechanical properties of bimrocks with high rock block proportion

For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fabricated bimrocks specimens.The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength,elastic modulus and block proportions of the bimrocks.In addition,the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions.Thus,three crucial indices(including matrix strength)are used as basic input parameters for the prediction of the mechanical properties of the bimrocks.Other than adopting the traditional simple regression and multi-regression analyses,a new prediction model based on the optimized general regression neural network(GRNN)algorithm is proposed.Note that,the performance of the multi-regression prediction model is better than that of the simple regression model,owing to the consideration of various influencing factors.However,the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does.Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.

Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

The Evaluation of Rural Territorial Functions: A Case Study of Henan, China

China＇s new urbanization process requires coordinated development between cities and rural areas. Territorial functions of rural areas are defined as advantageous effects on nature and human society that, in particular, rural systems perform via their property and interactions with other systems at certain social development stages. This paper establishes an index System for evaluating rural territorial functions including agricultural function, social function, economic function and ecological function. By establishing a model based on a General Regression Neural Network （GRNN） with the county-level as the basic unit, we comprehensively evaluate the rural territorial functions of 109 counties and/or cities in Henan province, China in 2000, 2005 and 2010. Results show that compared with that in 2000, each function in 2010 improved, with the spatial heterogeneity of economic func- tion the most evident, social service function comparatively balanced and spatial distribution of agricultural produc- tion function changing little. Cluster analysis was adopted to study the major functions of rural regions. Henan was divided into six major function zones to enhance administrative management and developmental policy. The six major function zones are Type I （core economic development zone）, Type II （agricultural production safeguarding zone）, Type III （function improving zone for rural areas）, Type IV （model zone of livelihood and social services）, Type V （economic restructuring and development zone）, and Type Vl （nature conservation areas）. Different development goals and development strategies should be considered according to different major function areas to achieve the coordinated development of urban and rural areas in China.