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Choose them as 1 1 2 2 3 3, , , , ,a b a b a bthe condition to determine them is that they should let the mathematical expectation of 2()ekget its minimum, that is ,we have the following equation 2min [ ( )]E e k （ 25） with the following constraint condition 1 1 1 1 2 2 2 2 3 3 3 30 , 。 (11) The calculation of *iT in above formulae with traditional methods is time consuming. Hence we use an RBF neural work to acplish the solution of *iT . This RBF neural work is the first part of the twostage RBF neural work proposed in the paper .It has only one output , ..ie , *iT ,and 2n inputs, that is ig , 1 in＃ and 0itt ,1 in＃ .Hn= hidden units are used here .Equation(11) is used to produce enough training samples. THE ONLINE PREDICTION OF THE COLD STORAGE TEMPERATURE One of the key problems of the optimum control over the storage temperature is how to predict the temperature accurately. Because of their robustness ,the prediction methods based on neural works have attracted more and more attentions. BP neural work is a kind of earlier used neural work for this purpose .But its training time is usually too long, and it has many local minimum points. Thus the RBF neural work has attracted more and more attention thanks to its higher training speed. This paper employs a twostage RBF neural work to predict the storage temperature..In the prediction process, the coupling relation between the temperature and the humidity should be taken into account. The paper selects the output variables in a way that the set of the variables include the temperature variables and the humidity variables at the same time. The choosing of the input variables should be taken into account no matter whether the control is performed or not, with the following two different cases involved: Case l: Automatic control system is off Suppose that there are R operating variables of the cold storage 1,... Ruu and S state variables 1,... a time window posed of 2Q time points, 1212( 1 ) , ( 2 ) , . . ., . . . Qt t Q t t t Q t t tt t t t t Q t+= D = D == + D = + D (12) Use ()qru and ()qsu to denote the values of ru and su at time point qt respectively (1 2 )qQ＃ . Let ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( ) ( ) ( ) ( )1 1 1 1( , .. ., , , .. ., , .. ., , .. ., , , .. . )Q Q Q Q TR S R Sx u u v v u u v v= (13) ( 1 ) ( 2 ) ( 1 ) 211( , ..., , ..., , ..., )Q Q Q Q TSSy v v v v++= (14) Where ( ) ,n R S Q m QS= + =. The task of the prediction is to determine y of (14)according to the vector x of (13) .For the current time 0t ,all of the measured results can be used to construct the inputs of the prediction work. Suppose that all of the operating variables and state variables can be got by measuring ,and their values in the future are unknown. To construct a prediction sample ,the related time t should satisfy 0t t Q t?D .Otherwise, unknown values would be contained in the sample which would be unreasonable. Suppose that enough samples ( ) ( )( , ), 1, 2 , .. .,kkx y k K= have been got .First, calculate the parameters of the hidden units, then calculate the prediction value of the storage temperature. Case2 :Automatic control system is on At this time, the set of the input variables only contains the environmental temperature, humidity and quantum of the stored fruits and vegetables ,etc. Any of the input variables doesn’t appear in the control algorithm ,while the prediction variables are the stable values of the state variables. The nonlinear mapping function of the RBF neural work is used to design the stable models. When the stable values of the state variables have been obtained, the control algorithm is used to calculate the temperature of the storehouse, thus the set of the predicted variables wouldn’t contain any variable to be controlled. That’s why the set of the predicted variables and the set of