SoloCodigo

Qué tal! Me llamo José Manuel y realizo el PFC sobre redes neuronales. Estoy un poco atascao y sólo haciendo esto, así q a ver si m podeis exar una manilla.

Resulta q tengo q simular el comportamiento de un transistor. Para ello, trabajo sobre una red sencillita, q os adjunto, y he de obtener ciertos pesos y bias de esta red para conseguir cierta función de transferencia entre la entrada y salida. Pues bien, m creo mi red, le meto los valores de entrenamiento medidos en el laboratorio y m salen unos errores mu curiosos. Por ejemplo, conecto la capa 6 con la 4 y al hacer net.LW{6,4}=1, m da un error (dice q la matriz esa debe ser nula. ¿Por qué, si esa conexión sí existe y la denoto con anterioridad en el código?).

Bueno, aquí os mando la red y el código (será una carilla a lo sumo, es sencillito), pa ver si veis algo q yo no veo, q es q llevo dándole vueltas mucho tiempo y lo mismo es algo tan evidente q ni m preocupo de ello. Si podeis, escribirme al correo mejor. Si no, contestad por aki. Un saludo y muchisimas gracias!!

jmanguasp@hotmail.com




******************************************************************
%**Aproximacion de la curva Ids frente a Vgs,Vds (Modelo Angelov)**
%******************************************************************

%********Transitor HEMT modelo foundry ED02AH con W=2*40***********

%************************Con 5 parametros**************************

net=network;                    

net.numInputs=2;                %dos entradas];  %primera entrada conectada a capa 1 y segunda entrada conectada a capas 3 y 5
net.layerConnect(2,1)=1;        %conexiones entre capa (ver red)
net.layerConnect(4,3)=1;
net.layerConnect(6,2)=1;
net.layerConnect(6,4)=1;
net.layerConnect(6,5)=1;
net.outputConnect(6)=1;
net.targetConnect(6)=1;

net.layers{1}.transferFcn='tansig';    %funciones de transferencia de cada capa (ver red)
net.layers{2}.transferFcn='purelin';
net.layers{3}.transferFcn='purelin';
net.layers{4}.transferFcn='purelin';
net.layers{5}.transferFcn='tansig';
net.layers{6}.transferFcn='purelin';
net.layers{6}.netInputFcn='netprod';

net.biasConnect=[1;1;0;1;0;0];  %biases que existen (ver red)

net=init(net);                  %inicializacion de los pesos
net.initFcn='initlay';
net.layers{1}.initFcn='initnw';    
net.layers{2}.initFcn='initnw';
net.layers{3}.initFcn='initnw';
net.layers{4}.initFcn='initnw';
net.layers{5}.initFcn='initnw';
net.layers{6}.initFcn='initnw';

%introduccion valores deseados (P=pareja Vgs,Vds y T=Ids) medidos en laboratorio
P=[[-2 ; 0] [-2 ; 0.1] [-2 ; 0.2] [-2 ; 0.3] [-2 ; 0.4] [-2 ; 0.5] [-2 ; 0.6] [-2 ; 0.7] [-2 ; 0.8] [-2 ; 0.9] [-2 ; 1] [-2 ; 1.1] [-2 ; 1.2] [-2 ; 1.3] [-2 ; 1.4] [-2 ; 1.5] [-2 ; 1.6] [-2 ; 1.7] [-2 ; 1.8] [-2 ; 1.9] [-2 ; 2] [-2 ; 2.1] [-2 ; 2.2] [-2 ; 2.3] [-2 ; 2.4] [-2 ; 2.5] [-2 ; 2.6] [-2 ; 2.7] [-2 ; 2.8] [-2 ; 2.9] [-2 ; 3] [-1.9 ; 0] [-1.9 ; 0.1] [-1.9 ; 0.2] [-1.9 ; 0.3] [-1.9 ; 0.4] [-1.9 ; 0.5] [-1.9 ; 0.6] [-1.9 ; 0.7] [-1.9 ; 0.8] [-1.9 ; 0.9] [-1.9 ; 1] [-1.9 ; 1.1] [-1.9 ; 1.2] [-1.9 ; 1.3] [-1.9 ; 1.4] [-1.9 ; 1.5] [-1.9 ; 1.6] [-1.9 ; 1.7] [-1.9 ; 1.8] [-1.9 ; 1.9] [-1.9 ; 2] [-1.9 ; 2.1] [-1.9 ; 2.2] [-1.9 ; 2.3] [-1.9 ; 2.4] [-1.9 ; 2.5] [-1.9 ; 2.6] [-1.9 ; 2.7] [-1.9 ; 2.8] [-1.9 ; 2.9] [-1.9 ; 3] [-1.8 ; 0] [-1.8 ; 0.1] [-1.8 ; 0.2] [-1.8 ; 0.3] [-1.8 ; 0.4] [-1.8 ; 0.5] [-1.8 ; 0.6] [-1.8 ; 0.7] [-1.8 ; 0.8] [-1.8 ; 0.9] [-1.8 ; 1] [-1.8 ; 1.1] [-1.8 ; 1.2] [-1.8 ; 1.3] [-1.8 ; 1.4] [-1.8 ; 1.5] [-1.8 ; 1.6] [-1.8 ; 1.7] [-1.8 ; 1.8] [-1.8 ; 1.9] [-1.8 ; 2] [-1.8 ; 2.1] [-1.8 ; 2.2] [-1.8 ; 2.3] [-1.8 ; 2.4] [-1.8 ; 2.5] [-1.8 ; 2.6] [-1.8 ; 2.7] [-1.8 ; 2.8] [-1.8 ; 2.9] [-1.8 ; 3] [-1.7 ; 0] [-1.7 ; 0.1] [-1.7 ; 0.2] [-1.7 ; 0.3] [-1.7 ; 0.4] [-1.7 ; 0.5] [-1.7 ; 0.6] [-1.7 ; 0.7] [-1.7 ; 0.8] [-1.7 ; 0.9] [-1.7 ; 1] [-1.7 ; 1.1] [-1.7 ; 1.2] [-1.7 ; 1.3] [-1.7 ; 1.4] [-1.7 ; 1.5] [-1.7 ; 1.6] [-1.7 ; 1.7] [-1.7 ; 1.8] [-1.7 ; 1.9] [-1.7 ; 2] [-1.7 ; 2.1] [-1.7 ; 2.2] [-1.7 ; 2.3] [-1.7 ; 2.4] [-1.7 ; 2.5] [-1.7 ; 2.6] [-1.7 ; 2.7] [-1.7 ; 2.8] [-1.7 ; 2.9] [-1.7 ; 3] [-1.6 ; 0] [-1.6 ; 0.1] [-1.6 ; 0.2] [-1.6 ; 0.3] [-1.6 ; 0.4] [-1.6 ; 0.5] [-1.6 ; 0.6] [-1.6 ; 0.7] [-1.6 ; 0.8] [-1.6 ; 0.9] [-1.6 ; 1] [-1.6 ; 1.1] [-1.6 ; 1.2] [-1.6 ; 1.3] [-1.6 ; 1.4] [-1.6 ; 1.5] [-1.6 ; 1.6] [-1.6 ; 1.7] [-1.6 ; 1.8] [-1.6 ; 1.9] [-1.6 ; 2] [-1.6 ; 2.1] [-1.6 ; 2.2] [-1.6 ; 2.3] [-1.6 ; 2.4] [-1.6 ; 2.5] [-1.6 ; 2.6] [-1.6 ; 2.7] [-1.6 ; 2.8] [-1.6 ; 2.9] [-1.6 ; 3] [-1.5 ; 0] [-1.5 ; 0.1] [-1.5 ; 0.2] [-1.5 ; 0.3] [-1.5 ; 0.4] [-1.5 ; 0.5] [-1.5 ; 0.6] [-1.5 ; 0.7] [-1.5 ; 0.8] [-1.5 ; 0.9] [-1.5 ; 1] [-1.5 ; 1.1] [-1.5 ; 1.2] [-1.5 ; 1.3] [-1.5 ; 1.4] [-1.5 ; 1.5] [-1.5 ; 1.6] [-1.5 ; 1.7] [-1.5 ; 1.8] [-1.5 ; 1.9] [-1.5 ; 2] [-1.5 ; 2.1] [-1.5 ; 2.2] [-1.5 ; 2.3] [-1.5 ; 2.4] [-1.5 ; 2.5] [-1.5 ; 2.6] [-1.5 ; 2.7] [-1.5 ; 2.8] [-1.5 ; 2.9] [-1.5 ; 3] [-1.4 ; 0] [-1.4 ; 0.1] [-1.4 ; 0.2] [-1.4 ; 0.3] [-1.4 ; 0.4] [-1.4 ; 0.5] [-1.4 ; 0.6] [-1.4 ; 0.7] [-1.4 ; 0.8] [-1.4 ; 0.9] [-1.4 ; 1] [-1.4 ; 1.1] [-1.4 ; 1.2] [-1.4 ; 1.3] [-1.4 ; 1.4] [-1.4 ; 1.5] [-1.4 ; 1.6] [-1.4 ; 1.7] [-1.4 ; 1.8] [-1.4 ; 1.9] [-1.4 ; 2] [-1.4 ; 2.1] [-1.4 ; 2.2] [-1.4 ; 2.3] [-1.4 ; 2.4] [-1.4 ; 2.5] [-1.4 ; 2.6] [-1.4 ; 2.7] [-1.4 ; 2.8] [-1.4 ; 2.9] [-1.4 ; 3] [-1.3 ; 0] [-1.3 ; 0.1] [-1.3 ; 0.2] [-1.3 ; 0.3] [-1.3 ; 0.4] [-1.3 ; 0.5] [-1.3 ; 0.6] [-1.3 ; 0.7] [-1.3 ; 0.8] [-1.3 ; 0.9] [-1.3 ; 1] [-1.3 ; 1.1] [-1.3 ; 1.2] [-1.3 ; 1.3] [-1.3 ; 1.4] [-1.3 ; 1.5] [-1.3 ; 1.6] [-1.3 ; 1.7] [-1.3 ; 1.8] [-1.3 ; 1.9] [-1.3 ; 2] [-1.3 ; 2.1] [-1.3 ; 2.2] [-1.3 ; 2.3] [-1.3 ; 2.4] [-1.3 ; 2.5] [-1.3 ; 2.6] [-1.3 ; 2.7] [-1.3 ; 2.8] [-1.3 ; 2.9] [-1.3 ; 3] [-1.2 ; 0] [-1.2 ; 0.1] [-1.2 ; 0.2] [-1.2 ; 0.3] [-1.2 ; 0.4] [-1.2 ; 0.5] [-1.2 ; 0.6] [-1.2 ; 0.7] [-1.2 ; 0.8] [-1.2 ; 0.9] [-1.2 ; 1] [-1.2 ; 1.1] [-1.2 ; 1.2] [-1.2 ; 1.3] [-1.2 ; 1.4] [-1.2 ; 1.5] [-1.2 ; 1.6] [-1.2 ; 1.7] [-1.2 ; 1.8] [-1.2 ; 1.9] [-1.2 ; 2] [-1.2 ; 2.1] [-1.2 ; 2.2] [-1.2 ; 2.3] [-1.2 ; 2.4] [-1.2 ; 2.5] [-1.2 ; 2.6] [-1.2 ; 2.7] [-1.2 ; 2.8] [-1.2 ; 2.9] [-1.2 ; 3] [-1.1 ; 0] [-1.1 ; 0.1] [-1.1 ; 0.2] [-1.1 ; 0.3] [-1.1 ; 0.4] [-1.1 ; 0.5] [-1.1 ; 0.6] [-1.1 ; 0.7] [-1.1 ; 0.8] [-1.1 ; 0.9] [-1.1 ; 1] [-1.1 ; 1.1] [-1.1 ; 1.2] [-1.1 ; 1.3] [-1.1 ; 1.4] [-1.1 ; 1.5] [-1.1 ; 1.6] [-1.1 ; 1.7] [-1.1 ; 1.8] [-1.1 ; 1.9] [-1.1 ; 2] [-1.1 ; 2.1] [-1.1 ; 2.2] [-1.1 ; 2.3] [-1.1 ; 2.4] [-1.1 ; 2.5] [-1.1 ; 2.6] [-1.1 ; 2.7] [-1.1 ; 2.8] [-1.1 ; 2.9] [-1.1 ; 3] [-1 ; 0] [-1 ; 0.1] [-1 ; 0.2] [-1 ; 0.3] [-1 ; 0.4] [-1 ; 0.5] [-1 ; 0.6] [-1 ; 0.7] [-1 ; 0.8] [-1 ; 0.9] [-1 ; 1] [-1 ; 1.1] [-1 ; 1.2] [-1 ; 1.3] [-1 ; 1.4] [-1 ; 1.5] [-1 ; 1.6] [-1 ; 1.7] [-1 ; 1.8] [-1 ; 1.9] [-1 ; 2] [-1 ; 2.1] [-1 ; 2.2] [-1 ; 2.3] [-1 ; 2.4] [-1 ; 2.5] [-1 ; 2.6] [-1 ; 2.7] [-1 ; 2.8] [-1 ; 2.9] [-1 ; 3] [-0.9 ; 0] [-0.9 ; 0.1] [-0.9 ; 0.2] [-0.9 ; 0.3] [-0.9 ; 0.4] [-0.9 ; 0.5] [-0.9 ; 0.6] [-0.9 ; 0.7] [-0.9 ; 0.8] [-0.9 ; 0.9] [-0.9 ; 1] [-0.9 ; 1.1] [-0.9 ; 1.2] [-0.9 ; 1.3] [-0.9 ; 1.4] [-0.9 ; 1.5] [-0.9 ; 1.6] [-0.9 ; 1.7] [-0.9 ; 1.8] [-0.9 ; 1.9] [-0.9 ; 2] [-0.9 ; 2.1] [-0.9 ; 2.2] [-0.9 ; 2.3] [-0.9 ; 2.4] [-0.9 ; 2.5] [-0.9 ; 2.6] [-0.9 ; 2.7] [-0.9 ; 2.8] [-0.9 ; 2.9] [-0.9 ; 3] [-0.8 ; 0] [-0.8 ; 0.1] [-0.8 ; 0.2] [-0.8 ; 0.3] [-0.8 ; 0.4] [-0.8 ; 0.5] [-0.8 ; 0.6] [-0.8 ; 0.7] [-0.8 ; 0.8] [-0.8 ; 0.9] [-0.8 ; 1] [-0.8 ; 1.1] [-0.8 ; 1.2] [-0.8 ; 1.3] [-0.8 ; 1.4] [-0.8 ; 1.5] [-0.8 ; 1.6] [-0.8 ; 1.7] [-0.8 ; 1.8] [-0.8 ; 1.9] [-0.8 ; 2] [-0.8 ; 2.1] [-0.8 ; 2.2] [-0.8 ; 2.3] [-0.8 ; 2.4] [-0.8 ; 2.5] [-0.8 ; 2.6] [-0.8 ; 2.7] [-0.8 ; 2.8] [-0.8 ; 2.9] [-0.8 ; 3] [-0.7 ; 0] [-0.7 ; 0.1] [-0.7 ; 0.2] [-0.7 ; 0.3] [-0.7 ; 0.4] [-0.7 ; 0.5] [-0.7 ; 0.6] [-0.7 ; 0.7] [-0.7 ; 0.8] [-0.7 ; 0.9] [-0.7 ; 1] [-0.7 ; 1.1] [-0.7 ; 1.2] [-0.7 ; 1.3] [-0.7 ; 1.4] [-0.7 ; 1.5] [-0.7 ; 1.6] [-0.7 ; 1.7] [-0.7 ; 1.8] [-0.7 ; 1.9] [-0.7 ; 2] [-0.7 ; 2.1] [-0.7 ; 2.2] [-0.7 ; 2.3] [-0.7 ; 2.4] [-0.7 ; 2.5] [-0.7 ; 2.6] [-0.7 ; 2.7] [-0.7 ; 2.8] [-0.7 ; 2.9] [-0.7 ; 3] [-0.6 ; 0] [-0.6 ; 0.1] [-0.6 ; 0.2] [-0.6 ; 0.3] [-0.6 ; 0.4] [-0.6 ; 0.5] [-0.6 ; 0.6] [-0.6 ; 0.7] [-0.6 ; 0.8] [-0.6 ; 0.9] [-0.6 ; 1] [-0.6 ; 1.1] [-0.6 ; 1.2] [-0.6 ; 1.3] [-0.6 ; 1.4] [-0.6 ; 1.5] [-0.6 ; 1.6] [-0.6 ; 1.7] [-0.6 ; 1.8] [-0.6 ; 1.9] [-0.6 ; 2] [-0.6 ; 2.1] [-0.6 ; 2.2] [-0.6 ; 2.3] [-0.6 ; 2.4] [-0.6 ; 2.5] [-0.6 ; 2.6] [-0.6 ; 2.7] [-0.6 ; 2.8] [-0.6 ; 2.9] [-0.6 ; 3] [-0.5 ; 0] [-0.5 ; 0.1] [-0.5 ; 0.2] [-0.5 ; 0.3] [-0.5 ; 0.4] [-0.5 ; 0.5] [-0.5 ; 0.6] [-0.5 ; 0.7] [-0.5 ; 0.8] [-0.5 ; 0.9] [-0.5 ; 1] [-0.5 ; 1.1] [-0.5 ; 1.2] [-0.5 ; 1.3] [-0.5 ; 1.4] [-0.5 ; 1.5] [-0.5 ; 1.6] [-0.5 ; 1.7] [-0.5 ; 1.8] [-0.5 ; 1.9] [-0.5 ; 2] [-0.5 ; 2.1] [-0.5 ; 2.2] [-0.5 ; 2.3] [-0.5 ; 2.4] [-0.5 ; 2.5] [-0.5 ; 2.6] [-0.5 ; 2.7] [-0.5 ; 2.8] [-0.5 ; 2.9] [-0.5 ; 3] [-0.4 ; 0] [-0.4 ; 0.1] [-0.4 ; 0.2] [-0.4 ; 0.3] [-0.4 ; 0.4] [-0.4 ; 0.5] [-0.4 ; 0.6] [-0.4 ; 0.7] [-0.4 ; 0.8] [-0.4 ; 0.9] [-0.4 ; 1] [-0.4 ; 1.1] [-0.4 ; 1.2] [-0.4 ; 1.3] [-0.4 ; 1.4] [-0.4 ; 1.5] [-0.4 ; 1.6] [-0.4 ; 1.7] [-0.4 ; 1.8] [-0.4 ; 1.9] [-0.4 ; 2] [-0.4 ; 2.1] [-0.4 ; 2.2] [-0.4 ; 2.3] [-0.4 ; 2.4] [-0.4 ; 2.5] [-0.4 ; 2.6] [-0.4 ; 2.7] [-0.4 ; 2.8] [-0.4 ; 2.9] [-0.4 ; 3] [-0.3 ; 0] [-0.3 ; 0.1] [-0.3 ; 0.2] [-0.3 ; 0.3] [-0.3 ; 0.4] [-0.3 ; 0.5] [-0.3 ; 0.6] [-0.3 ; 0.7] [-0.3 ; 0.8] [-0.3 ; 0.9] [-0.3 ; 1] [-0.3 ; 1.1] [-0.3 ; 1.2] [-0.3 ; 1.3] [-0.3 ; 1.4] [-0.3 ; 1.5] [-0.3 ; 1.6] [-0.3 ; 1.7] [-0.3 ; 1.8] [-0.3 ; 1.9] [-0.3 ; 2] [-0.3 ; 2.1] [-0.3 ; 2.2] [-0.3 ; 2.3] [-0.3 ; 2.4] [-0.3 ; 2.5] [-0.3 ; 2.6] [-0.3 ; 2.7] [-0.3 ; 2.8] [-0.3 ; 2.9] [-0.3 ; 3] [-0.2 ; 0] [-0.2 ; 0.1] [-0.2 ; 0.2] [-0.2 ; 0.3] [-0.2 ; 0.4] [-0.2 ; 0.5] [-0.2 ; 0.6] [-0.2 ; 0.7] [-0.2 ; 0.8] [-0.2 ; 0.9] [-0.2 ; 1] [-0.2 ; 1.1] [-0.2 ; 1.2] [-0.2 ; 1.3] [-0.2 ; 1.4] [-0.2 ; 1.5] [-0.2 ; 1.6] [-0.2 ; 1.7] [-0.2 ; 1.8] [-0.2 ; 1.9] [-0.2 ; 2] [-0.2 ; 2.1] [-0.2 ; 2.2] [-0.2 ; 2.3] [-0.2 ; 2.4] [-0.2 ; 2.5] [-0.2 ; 2.6] [-0.2 ; 2.7] [-0.2 ; 2.8] [-0.2 ; 2.9] [-0.2 ; 3] [-0.1 ; 0] [-0.1 ; 0.1] [-0.1 ; 0.2] [-0.1 ; 0.3] [-0.1 ; 0.4] [-0.1 ; 0.5] [-0.1 ; 0.6] [-0.1 ; 0.7] [-0.1 ; 0.8] [-0.1 ; 0.9] [-0.1 ; 1] [-0.1 ; 1.1] [-0.1 ; 1.2] [-0.1 ; 1.3] [-0.1 ; 1.4] [-0.1 ; 1.5] [-0.1 ; 1.6] [-0.1 ; 1.7] [-0.1 ; 1.8] [-0.1 ; 1.9] [-0.1 ; 2] [-0.1 ; 2.1] [-0.1 ; 2.2] [-0.1 ; 2.3] [-0.1 ; 2.4] [-0.1 ; 2.5] [-0.1 ; 2.6] [-0.1 ; 2.7] [-0.1 ; 2.8] [-0.1 ; 2.9] [-0.1 ; 3] [0 ; 0] [0 ; 0.1] [0 ; 0.2] [0 ; 0.3] [0 ; 0.4] [0 ; 0.5] [0 ; 0.6] [0 ; 0.7] [0 ; 0.8] [0 ; 0.9] [0 ; 1] [0 ; 1.1] [0 ; 1.2] [0 ; 1.3] [0 ; 1.4] [0 ; 1.5] [0 ; 1.6] [0 ; 1.7] [0 ; 1.8] [0 ; 1.9] [0 ; 2] [0 ; 2.1] [0 ; 2.2] [0 ; 2.3] [0 ; 2.4] [0 ; 2.5] [0 ; 2.6] [0 ; 2.7] [0 ; 2.8] [0 ; 2.9] [0 ; 3] [0.1 ; 0] [0.1 ; 0.1] [0.1 ; 0.2] [0.1 ; 0.3] [0.1 ; 0.4] [0.1 ; 0.5] [0.1 ; 0.6] [0.1 ; 0.7] [0.1 ; 0.8] [0.1 ; 0.9] [0.1 ; 1] [0.1 ; 1.1] [0.1 ; 1.2] [0.1 ; 1.3] [0.1 ; 1.4] [0.1 ; 1.5] [0.1 ; 1.6] [0.1 ; 1.7] [0.1 ; 1.8] [0.1 ; 1.9] [0.1 ; 2] [0.1 ; 2.1] [0.1 ; 2.2] [0.1 ; 2.3] [0.1 ; 2.4] [0.1 ; 2.5] [0.1 ; 2.6] [0.1 ; 2.7] [0.1 ; 2.8] [0.1 ; 2.9] [0.1 ; 3] [0.2 ; 0] [0.2 ; 0.1] [0.2 ; 0.2] [0.2 ; 0.3] [0.2 ; 0.4] [0.2 ; 0.5] [0.2 ; 0.6] [0.2 ; 0.7] [0.2 ; 0.8] [0.2 ; 0.9] [0.2 ; 1] [0.2 ; 1.1] [0.2 ; 1.2] [0.2 ; 1.3] [0.2 ; 1.4] [0.2 ; 1.5] [0.2 ; 1.6] [0.2 ; 1.7] [0.2 ; 1.8] [0.2 ; 1.9] [0.2 ; 2] [0.2 ; 2.1] [0.2 ; 2.2] [0.2 ; 2.3] [0.2 ; 2.4] [0.2 ; 2.5] [0.2 ; 2.6] [0.2 ; 2.7] [0.2 ; 2.8] [0.2 ; 2.9] [0.2 ; 3] [0.3 ; 0] [0.3 ; 0.1] [0.3 ; 0.2] [0.3 ; 0.3] [0.3 ; 0.4] [0.3 ; 0.5] [0.3 ; 0.6] [0.3 ; 0.7] [0.3 ; 0.8] [0.3 ; 0.9] [0.3 ; 1] [0.3 ; 1.1] [0.3 ; 1.2] [0.3 ; 1.3] [0.3 ; 1.4] [0.3 ; 1.5] [0.3 ; 1.6] [0.3 ; 1.7] [0.3 ; 1.8] [0.3 ; 1.9] [0.3 ; 2] [0.3 ; 2.1] [0.3 ; 2.2] [0.3 ; 2.3] [0.3 ; 2.4] [0.3 ; 2.5] [0.3 ; 2.6] [0.3 ; 2.7] [0.3 ; 2.8] [0.3 ; 2.9] [0.3 ; 3] [0.4 ; 0] [0.4 ; 0.1] [0.4 ; 0.2] [0.4 ; 0.3] [0.4 ; 0.4] [0.4 ; 0.5] [0.4 ; 0.6] [0.4 ; 0.7] [0.4 ; 0.8] [0.4 ; 0.9] [0.4 ; 1] [0.4 ; 1.1] [0.4 ; 1.2] [0.4 ; 1.3] [0.4 ; 1.4] [0.4 ; 1.5] [0.4 ; 1.6] [0.4 ; 1.7] [0.4 ; 1.8] [0.4 ; 1.9] [0.4 ; 2] [0.4 ; 2.1] [0.4 ; 2.2] [0.4 ; 2.3] [0.4 ; 2.4] [0.4 ; 2.5] [0.4 ; 2.6] [0.4 ; 2.7] [0.4 ; 2.8] [0.4 ; 2.9] [0.4 ; 3] [0.5 ; 0] [0.5 ; 0.1] [0.5 ; 0.2] [0.5 ; 0.3] [0.5 ; 0.4] [0.5 ; 0.5] [0.5 ; 0.6] [0.5 ; 0.7] [0.5 ; 0.8] [0.5 ; 0.9] [0.5 ; 1] [0.5 ; 1.1] [0.5 ; 1.2] [0.5 ; 1.3] [0.5 ; 1.4] [0.5 ; 1.5] [0.5 ; 1.6] [0.5 ; 1.7] [0.5 ; 1.8] [0.5 ; 1.9] [0.5 ; 2] [0.5 ; 2.1] [0.5 ; 2.2] [0.5 ; 2.3] [0.5 ; 2.4] [0.5 ; 2.5] [0.5 ; 2.6] [0.5 ; 2.7] [0.5 ; 2.8] [0.5 ; 2.9] [0.5 ; 3]];  
T=[0 0 0 0 0 0 0 0 0 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0 0 0 0 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0 0 0.0001 0.0001 0.0001 0.0002 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0003 0.0004 0.0004 0.0004 0.0004 0.0005 0.0005 0.0005 0.0005 0.0006 0.0006 0.0006 0.0006 0.0007 0.0007 0.0007 0.0007 0.0007 0.0008 0 0.0001 0.0001 0.0002 0.0003 0.0004 0.0004 0.0005 0.0005 0.0006 0.0007 0.0007 0.0008 0.0008 0.0009 0.001 0.001 0.0011 0.0011 0.0012 0.0012 0.0013 0.0014 0.0014 0.0015 0.0015 0.0016 0.0016 0.0017 0.0017 0.0018 0 0.0002 0.0003 0.0005 0.0006 0.0008 0.0009 0.0011 0.0012 0.0014 0.0015 0.0017 0.0018 0.0019 0.0021 0.0022 0.0023 0.0025 0.0026 0.0027 0.0028 0.003 0.0031 0.0032 0.0033 0.0035 0.0036 0.0037 0.0038 0.0039 0.004 0 0.0004 0.0008 0.0011 0.0015 0.0018 0.0022 0.0025 0.0028 0.0032 0.0035 0.0038 0.0041 0.0044 0.0048 0.0051 0.0054 0.0057 0.006 0.0063 0.0065 0.0068 0.0071 0.0074 0.0077 0.0079 0.0082 0.0085 0.0087 0.009 0.0093 0 0.0009 0.0017 0.0025 0.0034 0.0042 0.005 0.0057 0.0065 0.0073 0.008 0.0088 0.0095 0.0102 0.0109 0.0116 0.0123 0.013 0.0137 0.0144 0.015 0.0157 0.0163 0.017 0.0176 0.0182 0.0188 0.0195 0.0201 0.0207 0.0213 0 0.002 0.0039 0.0058 0.0077 0.0095 0.0114 0.0132 0.0149 0.0167 0.0184 0.0201 0.0218 0.0234 0.0251 0.0267 0.0283 0.0299 0.0314 0.033 0.0345 0.036 0.0375 0.039 0.0404 0.0419 0.0433 0.0447 0.0461 0.0475 0.0488 0 0.0045 0.009 0.0133 0.0176 0.0219 0.026 0.0302 0.0342 0.0382 0.0422 0.0461 0.05 0.0538 0.0575 0.0613 0.0649 0.0685 0.0721 0.0757 0.0792 0.0826 0.086 0.0894 0.0928 0.0961 0.0994 0.1026 0.1058 0.109 0.1121 0 0.0103 0.0205 0.0305 0.0403 0.05 0.0596 0.069 0.0783 0.0875 0.0966 0.1055 0.1144 0.1231 0.1317 0.1402 0.1486 0.1569 0.1651 0.1732 0.1812 0.1891 0.197 0.2047 0.2124 0.2199 0.2274 0.2348 0.2422 0.2494 0.2566 0 0.0235 0.0466 0.0693 0.0917 0.1138 0.1355 0.157 0.1782 0.1991 0.2197 0.2401 0.2602 0.28 0.2996 0.3189 0.3381 0.3569 0.3756 0.394 0.4123 0.4303 0.4481 0.4657 0.4831 0.5004 0.5174 0.5343 0.551 0.5675 0.5838 0 0.0526 0.1044 0.1554 0.2056 0.2552 0.304 0.3521 0.3996 0.4465 0.4928 0.5384 0.5835 0.628 0.672 0.7154 0.7583 0.8006 0.8425 0.8839 0.9248 0.9652 1.0052 1.0447 1.0837 1.1244 1.1606 1.1984 1.2359 1.2729 1.3095 0 0.1145 0.2272 0.3381 0.4474 0.5551 0.6613 0.7661 0.8694 0.9714 1.072 1.1713 1.2694 1.3662 1.4618 1.5563 1.6496 1.7417 1.8328 1.9228 2.0117 2.0997 2.1866 2.2726 2.3576 2.4417 2.5248 2.6071 2.6885 2.769 2.8487 0 0.2344 0.465 0.6918 0.9152 1.1355 1.3526 1.5668 1.778 1.9865 2.1922 2.3953 2.5957 2.7936 2.9891 3.1821 3.3728 3.5612 3.7474 3.9314 4.1132 4.293 4.4707 4.6464 4.8202 4.9921 5.1621 5.3303 5.4967 5.6613 5.8243 0 0.4351 0.8609 1.2779 1.6876 2.091 2.4886 2.8808 3.2676 3.6493 4.0259 4.3977 4.7647 5.1271 5.485 5.8384 6.1876 6.5325 6.8734 7.2103 7.5432 7.8723 8.1977 8.5194 8.8376 9.1523 9.4636 9.7715 10.0762 10.3776 10.6759 0 1.1927 2.1763 2.9713 3.6672 4.3183 4.9476 5.5638 6.1701 6.7678 7.3574 7.9394 8.5139 9.0811 9.6412 10.1945 10.741 11.2809 11.8144 12.3417 12.8628 13.3779 13.8872 14.3908 14.8889 15.3814 15.8686 16.3506 16.8274 17.2993 17.7662 0 5.1477 8.6615 10.7074 12.0102 13.0102 13.8925 14.7261 15.5357 16.3301 17.1124 17.8841 18.6458 19.3977 20.1403 20.8737 21.5981 22.3139 23.0211 23.7201 24.4109 25.0938 25.769 26.4366 27.0968 27.7497 28.3956 29.0345 29.6666 30.2921 30.9111 0 12.0627 19.9146 23.9851 26.1497 27.5473 28.657 29.6566 30.6095 31.5383 32.4508 33.3501 34.2375 35.1134 35.9784 36.8327 37.6766 38.5104 39.3342 40.1484 40.9531 41.7486 42.535 43.3127 44.0817 44.8423 45.5946 46.3389 47.0752 47.8038 48.5249 0 20.7322 33.985 40.522 43.6789 45.4813 46.781 47.8942 48.9333 49.9382 50.9227 51.8919 52.8479 53.7916 54.7233 55.6435 56.5526 57.4507 58.3381 59.2151 60.0819 60.9388 61.7859 62.6236 63.4519 64.2712 65.0816 65.8833 66.6765 67.4613 68.238 0 30.0915 49.1578 58.3248 62.5125 64.7091 66.1705 67.3629 68.4523 69.4969 70.5173 71.5207 72.5101 73.4866 74.4507 75.4029 76.3434 77.2727 78.1909 79.0983 79.9952 80.8819 81.7584 82.6251 83.4822 84.33 85.1685 85.998 86.8187 87.6308 88.4344 0 39.2571 64.0091 75.7376 80.9178 83.4821 85.0833 86.3349 87.4552 88.5207 89.5585 90.5779 91.5826 92.5741 93.533 94.5198 95.4748 96.4183 97.3505 98.2719 99.1825 100.0827 100.9727 101.8527 102.7229 103.5836 104.4349 105.2771 106.1104 106.9349 107.7509 0 47.5742 77.4826 91.5298 97.6036 100.4942 102.2148 103.5127 104.6537 105.7309 106.7772 107.804 108.8157 109.8139 110.7993 111.7726 112.734 113.6838 114.6223 115.5498 116.4665 117.3728 118.2687 119.1546 120.0307 120.8971 121.7542 122.602 123.4409 124.2709 125.0923 0 54.6683 88.9736 104.9963 111.8296 114.9956 116.815 118.1494 119.3051 120.3895 121.4401 122.4705 123.4852 124.4864 125.4748 126.4509 127.4151 128.3676 129.3089 130.2391 131.1585 132.0674 132.9659 133.8544 134.7331 135.602 136.4616 137.3119 138.1532 138.9857 139.8095 0 60.4719 98.3737 116.0117 123.4652 126.8552 128.7544 130.1174 131.284 132.3729 133.4261 134.4581 135.4743 136.4768 137.4664 138.4438 139.4092 140.363 141.3055 142.2368 143.1574 144.0674 144.9671 145.8576 146.7365 147.6066 148.4672 149.3186 150.161 150.9945 151.8194 0 64.7235 105.2601 124.0811 131.9885 135.5423 137.4994 138.8831 140.0571 141.1491 142.2037 143.2365 144.2534 145.2565 146.2467 147.2246 148.1905 149.1448 150.0878 151.0197 151.9408 152.8513 153.7515 154.6416 155.5219 156.3924 157.2536 158.1054 158.9483 159.7823 160.6076 0 65.5112 106.5359 125.5762 133.5679 137.1521 139.1201 140.5078 141.6834 142.7761 143.831 144.8642 145.8813 146.8846 147.8751 148.8532 149.8194 150.774 151.7172 152.6493 153.5706 154.4813 155.3818 156.2721 157.1525 158.0233 158.8846 159.7367 160.5797 161.4139 162.2394];
   
net.performFcn='mse';           %funcion de actuacion (Mse) --> aprox. de funciones

%entrenamos la red
net=newff(minmax(P),[1 1 1 1 1 1 ],{'tansig' 'purelin' 'purelin' 'purelin' 'tansig' 'purelin'},'trainlm');
net.trainParam.goal=0.000001;
net.trainParam.epochs=1000;
net.trainParam.show=100;
net=train(net,P,T);

net.b{2}=1;                     %biases capa 2 y 4 valen 1 (ver red)
net.b{4}=1;

net.LW{2,1}=1;                  %pesos inicializados a 1 (ver red)
net.LW{4,3}=1;
net.LW{6,4}=1;                  %al simular me dice q este peso debe ser una matriz vacia: POR QUE?????
net.LW{6,5}=1;

Y=sim(net,P);

correlacion=corrcoef(Y,T)
pause;

[m,b,r]=postreg(Y,T)
pause;
figure;

%representacion grafica de los datos
P1=[-2 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5];
P2=[0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3];

Datos=reshape(T,length(P2),length(P1));
Sim=reshape(Y,length(P2),length(P1));

mesh(P1,P2,Datos);              %datos teoricos
figure;

mesh(P1,P2,Sim);                %datos tras la simulacion


%obtencion de los pesos y bias deseados (POR QUE SALEN ERRORES EN ALGUNOS????)
Q1=net.b{1,1}                    %-Q1

Hola. Kiero fijar unos determinados pesos y bias de una red neuronal a 1. ¿Cómo lo hago? He probado con varias cosas pero no sale. Si alguien sabe algo, responda por akí o a mi correo (jmanguasp@hotmail.com). Un saludo y gracias!

Tengo un vector dado por

A=[-2 -1.9 -1.8 -1.7 -1.6 ... 0.2 0.3 0.4 0.5]

Otro dado por

B=[0 0.1 0.2 0.3 0.4 ... 2.8 2.9 3]

Y otro C, formado por unos valores medidos experimentalmente al combinar A con B. Es decir, para [-2 0] un valor de C; para [-2 0.1] otro valor de C; para [-2 0.2] otro y, así, sucesivamente. ¿Cómo puedo representar una gráfica en 3D en la q en cada eje este uno de estos vectores?

PD: he usado PLOT3(A,B,C) pero me dice q no se puede usar porque los vectores no son del mismo tamaño!!

Hola, tengo una red con dos entradas y una salida. Las entradas son Vgs, con rango [-2 0.5] y Vds, con rango [0 3] (ambas entradas van variando de 0.1 en 0.1). Así, tendré diferentes valores de salida para las diferentes combinaciones de entrada. Es decir, un valor de salida para el par de entrada [-2 0], otro para [-2 0.1], [-2 0.2],...,[-2 3],[-1.9 0],[-1.9 0.1],...etc. ¿cómo se pone esto usando el toolbox de redes neuronales? Gracias!

Hola, q tal? Necesito saber si hay una función, q creo q no, en el toolbox de redes neuronales de matlab q haga y=exp(x^2). Lo he buscado y no lo he encontrao pero he visto q hay la posibilidad de crear una función de transferencia propia con 'mytf'. Así, para crear lo q necesito, tendría q dejar mytf tal cual salvo la línea en la q se expresa la relación entre 'a' y 'n' y poner ahí a=exp(n^2)??? Un saludo

Hola, soy José Manuel, alumno de telecomunicaciones de la Universidad de Sevilla. Estoy haciendo el PFC sobre redes neuronales y tengo ciertos problemas. Por ejemplo, no sé cómo se pone una exponencial cuadrática o una suma ponderada como funciones de transferenca. A ver si m podeis echar una mano. Gracias!!
PD: os envio el código pa q veais lo q kiero hacer