由於全球化的迅速發展使得供應鏈管理更加複雜，也有越來越多的公司採用無線射頻辨識系統（RFID）於倉儲管理。RFID有高速掃描，滲透性，記憶性等優點。除了能夠回收，利用RFID系統還可以用於辨識商品與揀貨車的位置來降低企業成本。
因此，本研究擬整合人工免疫系統（AIS）和最佳化人工免疫網絡（opt-aiNET）來訓練倒傳遞神經網路（aiNBSB）。目的使神經網路能夠學習接收信號強度指示（RSSI）與揀貨車的位置關係。由於aiNBSB同時擁有AIS與opt-aiNET的優點，能夠避免掉入區域最佳解且仍能具備學習的能力。經由兩個連續型問題的計算結果顯示該演算法具有比其它基於AIS的倒傳遞神經網路有更好的效能表現。此外，模型經由實際實驗結果顯示，該演算法確實比其它方法能更夠有效預測撿貨車的位置。

Due to rapid development of globalization which makes the supply chain management more complicated, there are more and more companies applying radio frequency identification (RFID) to warehouse management. The obvious advantages of RFID are high-speed scanning, penetrating and memorable. In addition to recycling, using the RFID system can also reduce business costs in indentifying the position of goods and picking carts.
Therefore, this study intends to integrate artificial immune system (AIS) and artificial immune network for optimization (opt-aiNET) for training back-propagation neural network (aiNBSB). The proposed neural network is able to learn the relationship between the received signal strength indication (RSSI) and picking cart position. Since the proposed aiNBSB owns both the merits of AIS and opt-aiNET, it is able to avoid falling into the local optimum and possesses the learning capability. The computational results for learning two continuous functions show that the proposed algorithm has better performance than other AIS-based back-propagation neural network. In addition, the model evaluation results also indicate that the proposed algorithm really can predict the picking cart position more correctly than other methods.

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