Function Approximation with AI methodologies

The first one is about using neural networks to solve a calculation with complicated environment.
The title is <Application of artificial neural networks to calculate the partial gas concentrations in a mixture>
http://www.sciencedirect.com/science/article/pii/S0925400501007365

The second one is about using neural networks to forecast.
The title is <Bankruptcy prediction using neural networks>
http://www.sciencedirect.com/science/article/pii/0167923694900248

The final one is about how neural networks learn from experience.
http://books.google.co.uk/books?hl=zh-CN&lr=&id=FJblV_iOPjIC&oi=fnd&pg=PA181&dq=neural+networks+for+calculate&ots=zYIj3gKYQS&sig=VBXX2F_YjXnzNRSY4G5ptcJjpEs&redir_esc=y#v=onepage&q=neural%20networks%20for%20calculate&f=false

I think these three part are the main development directions of neural networks.

P= [-0.4 -0.5 0.6; 0.9 0 0.1];
T= [1 1 0];

net=newp([-1 1;-1 1],1);
net.trainParam.epochs = 20;
net=train(net,P,T);    
Y=sim(net,P)      
E1=mae(Y-T)    
Q=[0.6 0.9 -0.1; -0.1 -0.5 0.5];
Y1=sim(net,Q)      
figure;          
plotpv(Q,Y1);      
plotpc(net.iw{1},net.b{1})



P=[0.5152 0.8173 1.0000 ;
     0.8173 1.0000 0.7308;
     1.0000 0.7308 0.1390;
     0.7308 0.1390 0.1087;
     0.1390 0.1087 0.3520;
     0.1087 0.3520 0.0000;]';
T=[0.7308 0.1390 0.1087 0.3520 0.0000 0.3761];
net=newff([0 1;0 1;0 1],[5,1],{'tansig','logsig'},'traingd');
net.trainParam.epochs=5000;
net.trainParam.goal=0.01;
LP.lr=0.1;
net=train(net,P,T);

newp():generate a perceptron
hardlim():hard limiter activation function
learnp():perceptron learning function
train():neural network training function
sim():neural network simulation function
mae():mean absolute error performance function