<ul>
*　　　　　　　<li>界说变量与参数：输入变量x、权值变量w、偏置因子b，实践输出y、期望输出d和学习率参数c</li>
*　　　　　　　<li>初始化：n和权值w</li>
*　　　　　　　<li>输入练习样本，对每个练习样本拟定期望输出</li>
*　　　　　　　<li>核算实践输出y=sgn(w*x+b)</li>
*　　　　　　　<li>更新权值向量w（n+1）=w（n）+c[d-y（n）]*x（n）</li> 0<c<1
*　　　　　　　<li>判别，假如满足收敛条件，算法完毕。不然回来3</li>(* <li> definition of variables and parameters: input variables X, W, variable power offset factor B, y, D output practice expected output and learning rate parameter c</li>

* <li> initialization: N and weight w</li>

* <li> input practice sample, for each sample to exercise the expected output of </li>

* <li> accounting practice output y=sgn (w*x+b) </li>

* <li> update the weight vector w (n+1) =w (n) +c[d-y (n)]*x (n) </li> 0<c<1

* <li> discriminant, if meet the convergence condition, the algorithm is completed. Come back, 3</li>)