Recognition of facial expressions - Knowledge Based Systems ...

• The basic vanilla version back-propagation algorithm minimizes the
squared error cost function and uses the three-layer elementary back
propagation topology. Also known as the generalized delta rule.
Advantages
- it is capable of storing many more patterns than the number of FA dimensions
- it is able to acquire complex nonlinear mappings
Limitations
- it requires extremely long training time
- offline encoding
- inability to know how to precisely generate any arbitrary mapping procedure
The C++ class that handles the operations related to the ANN is presented in Listing 3.
//-----------------------------
1: class nn
2: {
3: model&m;
4: int ni,nh,no;
5: float i[NI],h[NH],o[NO],eh[NH],eo[NO],w1[NI][NH],w2[NH][NO];
6: public:
7: nn(model&,int,int,int);
8: void train(void);
9: void test(void);
10: void save(char*);
11: void load(char*);
12: private:
13: void randomWeights(void);
14: float f(float);
15: float df(float);
16: void pass();
17: float trainSample(int);
18: };
//-----------------------------
Listing 3. C++ class to handle the ANN
The class presented offers the possibility to save a defined structure of ANN including
the weights and to load an already developed one. The source of the ANN routines is
presented in the appendix.
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