"treatise On The" Side - Card Elements Corresponding To The Construction Of The Mathematical Model Of The System And Its Neural Network

This study coupled both new system of syndrome-essential-factor and formula-essential-factor with the artificial intelligence technique to analyze all the formula of the Shang Han Lun to build a mathematical model of formula-syndrome essential-factor correspondence and formula-syndrome correspondence. These are introduced as follow.Purpose1.To establish the structural analyses of Shang Han Lun and establish the systems of formula-syndrome essential-factor correspondence and formula-syndrome correspondence.2. To establish the mathematical model of formula-syndrome correspondence with artificial neural network technique.3. To establish the mathematical model of formula-syndrome essential-factor correspondence with artificial neural network technique. mathematical model: Yj=output vector, the formula essential-factor (or herb) Xi=input vector, the syndrome essential-factor (or syndrome) f=the transfer function of artificial neural network model Wij= weight Valuesθj= Bias ValuesMethods1. It is developed by self-coding with the software of Microsoft Visual Studio C#. NET 2008, SQL Server 2008 and Access 2007 to design all the functions that we need.2. One of the method called Back Propagation Network (BPN) of the artificial neural network technique was employed to build the mathmatiocal model.3.All the data in the system of formula-syndrome essential-factor correspondence and formula-syndrome correspondence were established and then imported into the mathematical model of artificial neural network 4. The weight and bias values were calculated by reiteration training to produce the new mathematical model of systems of formula-syndrome essential-factor correspondence and formula-syndrome correspondence.Results and Discussion1.This study employed the book of伤寒论讲义by Professor Wang Qingguo (2007) for the materials to undertake the structural analyses of Shang Han Lun. There are many information could be extracted from these structural analyses, i. e. formula essentialfactor, syndrome essential factor, formula-syndrome essential factor correspondence and formula syndrome correspondence.2. To establish the mathematical model, Yj=f(∑wijxi_θj), of formula syndrome correspondence with artificial neural network technique and apply it to the clinical therapy of prescription.Method:The data of structural analyses of Shang Han Lun was imported into the x-vecter and y-vector and then transferred into artificial neural network system to calculate the weight and bias values. If the error of Root-Mean-Square Value is less 0.06 among the iteration of 5000, the optimum solution of the weight and bias values are done. All the system was done at the IBM PC with Windows XP. Every calculation at least nedd 60 minutes to finish the itereation.Result:These results reveal that most of time it could get the optimum solution of the weight and bias values. The mathematical model then be applied to practical prediction of prescription. Those demonstration shows that it is feasible and practical to forecast the precise formulas for the patients. However, some divergence appear while the amount of neuron node is too large. Two methods were employed to imporved the efficiency of convergence, i.e. the adjustment of learning rate and the conjugate gradient method. The systematic dimension could also improved from (219×68) to (258×74). The input samples could also improved from 75 to 88.3. To establish the mathematical model, Yj=f(∑wijxi_θj),of formula-syndrome essential-factor correspondence with artificial neural network technique and apply it to the clinical therapy of prescription.Method:The data of structural analyses of Shang Han Lun was imported into the x-vecter and y-vector and then transferred into artificial neural network system to calculate the weight and bias values. If the error of Root-Mean-Square Value is less 0.06 among the iteration of 5000, the optimum solution of the weight and bias values are done. All the system was done at the IBM PC with Windows XP. Every calculation at least nedd 60 minutes to finish the itereation.Result:These results reveal that most of time it could get the optimum solution of the weight and bias values. The mathematical model then be applied to practical prediction of prescription. Those demonstration shows that it is feasible and practical to forecast the precise formulas for the patients. However, some divergence appear while the amount of neuron'node is too large. Three methods were employed to imporved the efficiency of convergence, i.e. the adjustment of learning rate, the conjugate gradient method and importing the previous optimum solution as current default original vector. The systematic dimension could also improved from (129×72) to (134×74). The input samples could also improved from 196 to 204.4. There are many synonym with the similar terms of syndrome appear while we establish the structural analyses of Shang Han Lun for the systems of formula-syndrome essential-factor correspondence and formulasyndrome correspondence. Because the meanings of the syndrome term will has the significant influence on the caluculation of weight and bias values.It is necessary to deal with the unity and simplification for these syndrome terms.5. The efficiency of convergence for the system of formula-syndrome essential-factor correspondence is better than the one of formula syndrome correspondence. The first reason is because it is a smaller scope of correspondence relation for the system of ormula-syndrome essential-factor correspondence. But it is a more complex correspondence relation for the system of formula syndrome correspondence. The second reason is because it is different for the amount of neuron nodes. The systemic dimension of formula-syndrome essential-factor correspondence is 160x160, but that for formula syndrome correspondence is 354x98. It is harder to calculate the optimum solution of the weight and bias values.