Neuro-Fuzzy Predictive Control Policies in Dynamic Input-Output Model of Japanese Industrial Structure

Y. Ito (Japan)


Predictive control, Dynamicinputoutput system, Neurofuzzy algorithm,Econometric models, Japan, Simulation


This paper deals with an application of predictive optimal control policy to dynamic input-output systems of Japanese large-scale industrial (primary, secondary and tertiary) sectors by neuro-fuzzy algorithm. The predictive control policy has three steps. The first is to obtain the optimal control policy such as the minimization of the weighted sum of the squared deviation between the actual targets and the desired subject to econometric models. The second is to determine the optimal outputs for each industrial sector through Dynamic Input-Output (abbr. DIO) system under the optimal control policies. The third is to obtain the network outputs by neuro-fuzzy algorithm through the controlled output equations derived from DIO system. We can see what affects the outputs if the optimal control policy was adopted, and how the change of industrial structure has occurred after the bubble burst in 1990’s in Japan during 1985 through 1993 by using DIO linked to the final demand econometric models of the Japanese industrial sectors by simulation.

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