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首頁> 外國專利> Farming Portfolio Optimization with Cascaded and Stacked Neural Models Incorporating Probabilistic Knowledge for a Defined Timeframe

Farming Portfolio Optimization with Cascaded and Stacked Neural Models Incorporating Probabilistic Knowledge for a Defined Timeframe

機(jī)譯：在定義的時(shí)間范圍內(nèi)結(jié)合概率知識(shí)的級(jí)聯(lián)和堆疊神經(jīng)模型進(jìn)行農(nóng)業(yè)投資組合優(yōu)化

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Optimizing the allocation of farmland between different crops is provided. First and second Deep Boltzmann machines (DBMs) are built, wherein the hidden layers of the DBMs are split into a plurality of neural networks, each neural network modeling a different timeframe of crop growth. A plurality of factors related to crop growth are fed into the first DBM, which is trained to produce a first multi-class output of predicted maximum crop yields within a specified overall timeframe. The first multi-class output is fed into the second DBM, which is trained to produce a second multi-class output of predicted crop yields. The second multi-class output is fed into a decision support system that generates a recommended allocation of the farmland among different crops during different timeframes to maximize total yield.
機(jī)譯：提供了不同作物之間農(nóng)田的優(yōu)化分配。建立第一和第二臺(tái)Deep Boltzmann機(jī)器（DBM），其中將DBM的隱藏層分為多個(gè)神經(jīng)網(wǎng)絡(luò)，每個(gè)神經(jīng)網(wǎng)絡(luò)都模擬了作物生長的不同時(shí)間范圍。與作物生長有關(guān)的多種因素被輸入到第一DBM中，該DBM經(jīng)過訓(xùn)練可以在指定的總體時(shí)間范圍內(nèi)生成預(yù)測最大作物產(chǎn)量的第一多類輸出。第一個(gè)多類輸出被饋送到第二個(gè)DBM中，第二個(gè)DBM被訓(xùn)練為產(chǎn)生預(yù)測作物產(chǎn)量的第二個(gè)多類輸出。第二個(gè)多類輸出被饋送到?jīng)Q策支持系統(tǒng)，該系統(tǒng)在不同的時(shí)間范圍內(nèi)在不同作物之間產(chǎn)生建議的耕地分配，以最大程度地提高總產(chǎn)量。

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