Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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A 'Zero Energy Cool Chamber (ZECC)' has been developed for storing fruits and vegetables from the viewpoints of low cost and energy savings. Adding water to a filler between the outer and inner brick walls and shade curtains is effective way to reduce the inside temperature of a ZECC. The objective of this study was to minimize the inside temperature by controlling the watering using an intelligent optimization technique (IOT) combined with neural networks (NN) and genetic algorithms (GA). The objective function was given by the average value of the inside temperature for one day. For dynamic optimization, the control process (24h) was divided into 8 steps, and the optimal value (8-step ON-OFF intervals) of watering was obtained using NN and GA. In this method, dynamic changes in the inside temperature of the ZECC, as affected by the watering strategy, outside temperature and inside relative humidity conditions, were first identified using NN, and then the optimal value, which minimized the objective function, was determined through simulation of the identified NN model using GA. The average inside temperature for this optimal control was 4^oC lower than that for the continuous watering for 24h, and was also 7.5^oC lower than that for no watering. The ZECC with the optimal watering strategy extended the shelf-life of tomato from 7 to 16days. Thus, it was concluded that a ZECC optimized by using NN and GA is useful for storing tomato with no electric energy.