MASON代表多主體鄰里或網(wǎng)絡(luò)仿真(Multi-Agent Simulator of Neighborhoods or NetWorks)。它是喬治梅森大學(xué)用Java開發(fā)的離散事件多主體仿真核心庫，具有快速、靈活和便攜的特點(diǎn)。它本身支持輕量級(jí)的模擬需求，自含模型可以嵌入到其他Java應(yīng)用當(dāng)中，還可以選擇2D和3D圖形顯示。
Single-layer neural NetWorks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural NetWorks while the third can be generalized to multi-layer perceptrons.
