本文選題：土壤墑情預(yù)測(cè)系統(tǒng) + 灰狼優(yōu)化算法��； 參考：《計(jì)算機(jī)應(yīng)用》2017年04期

【摘要】：針對(duì)現(xiàn)有的固定端傳感器土壤墑情監(jiān)測(cè)預(yù)測(cè)系統(tǒng)架設(shè)成本高、傳感器易損壞、預(yù)測(cè)精度較低等問(wèn)題,設(shè)計(jì)并實(shí)現(xiàn)了基于非固定無(wú)線傳感器組網(wǎng)與改進(jìn)灰狼算法優(yōu)化神經(jīng)網(wǎng)絡(luò)的土壤墑情監(jiān)測(cè)預(yù)測(cè)系統(tǒng)。系統(tǒng)使用非固定即插即用式傳感器藍(lán)牙組網(wǎng)收集墑情數(shù)據(jù),使用高精度多源定位接入融合方法進(jìn)行廣域室外高精度定位。在算法方面,針對(duì)灰狼算法在迭代中后期易陷入局部最優(yōu)等問(wèn)題,提出一種基于末尾探索者策略的改進(jìn)灰狼算法。首先,根據(jù)種群個(gè)體適應(yīng)度值排名,在原有算法個(gè)體類(lèi)型中增加探索者類(lèi)型。然后,將種群搜索分為三個(gè)時(shí)期:活躍探索期、周期探索期和種群回歸期。最后,在每個(gè)時(shí)期使用特有的位置更新策略進(jìn)行探索者位置調(diào)整,使得算法在探索初期更具隨機(jī)性,在探索中后期依然保持一定的解空間搜索能力,從而增強(qiáng)算法的局部最優(yōu)回避能力。使用標(biāo)準(zhǔn)函數(shù)進(jìn)行算法性能測(cè)試,并將該算法應(yīng)用于優(yōu)化土壤墑情神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型問(wèn)題,使用某市2號(hào)試驗(yàn)田的數(shù)據(jù)進(jìn)行實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,所提算法與直接神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型相比,相對(duì)誤差下降約4個(gè)百分點(diǎn);與傳統(tǒng)灰狼算法、粒子群優(yōu)化(PSO)算法優(yōu)化模型比較,相對(duì)誤差下降約1至2個(gè)百分點(diǎn)。所提算法擁有更小的誤差,更好的局部最優(yōu)回避能力,能有效提高墑情的預(yù)測(cè)質(zhì)量。
[Abstract]:Aiming at the problems of high installation cost, easy damage of sensors and low precision of prediction, the existing fixed end sensor system for monitoring and forecasting soil moisture content is high. A soil moisture monitoring and forecasting system based on non-fixed wireless sensor networking and improved gray wolf algorithm optimization neural network is designed and implemented. The system uses non-fixed plug and play sensor Bluetooth network to collect moisture data, and uses high-precision multi-source positioning access fusion method to carry out wide-area outdoor high-precision positioning. In the aspect of algorithm, an improved gray wolf algorithm based on the end seeker strategy is proposed to solve the problem that the gray wolf algorithm is prone to fall into local optimum in the middle and late stage of iteration. Firstly, according to the rank of population individual fitness, the seeker type is added to the individual type of the original algorithm. Then, the population search is divided into three periods: active exploration period, periodic exploration period and population regression period. Finally, the special location updating strategy is used to adjust the location of the explorers in each period, which makes the algorithm more random in the initial stage of exploration, and still maintains a certain ability of solving space search in the middle and late stages of exploration. Thus the local optimal avoidance ability of the algorithm is enhanced. The standard function is used to test the performance of the algorithm, and the algorithm is applied to optimize the prediction model of soil moisture. The experimental results show that the relative error of the proposed algorithm is about 4 percentage points lower than that of the direct neural network prediction model, and that of the particle swarm optimization (PSO) algorithm is about 1 to 2 percentage points lower than that of the traditional grey wolf algorithm. The proposed algorithm has smaller error and better local optimal avoidance ability, which can effectively improve the prediction quality of soil moisture.
【作者單位】： 北京郵電大學(xué)電子工程學(xué)院;
【基金】：國(guó)家科技支撐計(jì)劃項(xiàng)目(2014BAD10B06)~~
【分類(lèi)號(hào)】：TP18;TP212.9

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本文編號(hào)：1934431