上图是前馈网络基本结构，分别介绍几种前馈网络：
M-P model，single-layer perceptron network,multi-layer perceptron network, BP(back propagation)
(一）M-P model
Proposed by McCulloch & PittsConsists of fixed Structure & fixed Link-WeightsRestrain Convex-Touch Weight ⇒ output is 0Exciting Convex-Touch Weight ⇒ output is 1 when the accumulated value ≥θ
Advantages:Simplest Feed-forward NNCan implement some Logical Operational FuncDisadvantages:I/O & weight must be discrete values: 0 or 1Link-Weight can not be adjusted dynamically
(二）single-layer perceptron network
supervised method可调权重
这里y=f(s)也可以是其他函数，根据具体情况，因为这里是用来简单分类，所以sign函数就够了。
然后计算权重，使用delta study principle。
分类结果输出。
缺点：Only linear classifiable patterns can be classified by Single-Layer Perceptron
linear non-classifiable patterns cannot be classified by Single-Layer Perceptron
