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- W2328771007 abstract "The present work attempted to apply an intelligent control technique including neural networks and genetic algorithms to the optimization of fruit storage process. In the method, water loss of a fruit as affected by relative humidity was first identified using neural networks, and then the optimal setpoints of relative humidity which minimize the objective functions were searched for through simulation of the identified model. Two sorts of objective functions were considered. One is the sum of water loss and the degree of disease of a fruit. The other is the error between the desired values and the current values of water loss. Both are minimization problems. A three layer neural network was effective for building the dynamic model of the water loss of a fruit to relative humidity. A genetic algorithm with high crossover and mutation rates allowed the optimal setpoints of relative humidity to be quickly sought from simulation of the identified model. Thus, a new intelligent control technique combining with neural networks and genetic algorithms was shown to be effective for the optimization of fruit storage process." @default.
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- W2328771007 date "1995-01-01" @default.
- W2328771007 modified "2023-09-24" @default.
- W2328771007 title "Intelligent Control Simulation for Optimization of Storage Process." @default.
- W2328771007 doi "https://doi.org/10.2525/jshita.7.91" @default.
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