Development of Generlaized Neuron and its variations:

1. Original Implementation: Generalized Neuron (GN) model c-code
   (GN.c)


2. GN Variations:
1. GN Model0 (GN_model0.c) output = osigma*wsigma + opi*wpi
2. GN Model1 (GN_model1.c) output = osigma*wsigma + opi * (1-wsigma)
3. GN Model2 (GN_model2.c) output = (Osigma^wsigma)* (opi ^ (1-wsigma))
4. GN Model3 (GN_model3.c) output = osigma * wsigma + (osigma+opi-osigma*opi)*(1- wsigma)
5. GN Model4 (GN_model4.c) output = (osigma ^ wsigma) * ((osigma+opi- osigma*opi)^(1-wsigma))
6. GN Model5 (GN_model5.c) output = sqrt(osigma*opi)*wsigma + (osigma+opi)*(1- wsigma)/2.0
7. GN Model6 (GN_model6.c) output = sqrt(osigma*opi)^(1- wsigma)* (osigma+opi)/2.0) ^ (1- wsigma)
8. GN Model7 (GN_model7.c)


3. Revision: Generlaized Matlab ver. 6.5 Code GN1.m


4. Benchmark Testing: Benchmark.m
1. Parity Problem
2. Voting Problem
3. Spiral Problem
4. Mackey Glass Problem


5. GN Applications in:
1. Machine Modeling
2. Power System Control
3. Electrical Load Forecasting
4. Channel Equalization
5. Robotic Control
6. Air craft Landing Control