Inside each neuron X=input, y=output X( (=2)1(=2) x(=)w( y=f( u)with u=∑v(x小+b, b=bias, x=input, w= weight, u= internal signal Typically fo is a logistic(sigmod)function,i. e f(u) assume B=l for simplicity 1+e therefore=f(u) )x(+b 1+e Neural Networks Ch9, ver. 9bInside each neuron x=input, y=output Neural Networks Ch9. , ver. 9b • 17           −  + − = = = = + = = = + = = = = = = =  + i I i i x i u i I i e y f e f u f b x w u y f u w(i)x(i) b 1 ( ) ( ) b 1 1 1 therefore (u) ,assume 1for simplicity, 1 1 ( ) Typically ()is a logistic (sigmod) function, i.e. bias, input, weight, internalsignal (u)with ,     x(i = 1) y w(i = 1) u f (u) w(I) x(i = 2) w(i = 2) x(i = I)