In ANFIS, the number of hidden nodes in neural networks is as well as in a fuzzy system which consists of fuzzification (layer-1), fuzzy inference system (layer-2 and layer-3), defuzzification (layer-4) and aggregation (layer-5).
For the 4DoF case, there will be 4 ANFIS networks, one for each theta parameter(theta1, theta2, theta3, theta4) as follows:
- The first ANFIS network will be trained with X and Y coordinates as input and corresponding theta1 values as output. The matrix data1 contains the x-y-theta1 dataset required to train the first ANFIS network(data1 will be used as the train)
- The second ANFIS network will be trained with X and Y coordinates as input and corresponding theta2 values as output. The matrix data2 contains the x-y-theta2 dataset(data2 will be used as the train)
- The third ANFIS network will be trained with X and Y coordinates as input and corresponding theta3 values as output. The matrix data3 contains the x-y-theta3 dataset(data3 will be used as the train)
- The fourth ANFIS network will be trained with X and Y coordinates as input and corresponding theta4 values as output. The matrix data4 contains the x-y-theta4 dataset(data4 will be used as the train)
Angles and leghts:
| theta1 | theta2 | theta3 | theta4 |
|---|---|---|---|
| -2pi:2pi | 0:pi/2 | -pi/4:pi/4 | -pi/2:pi/2 |
| l1 | l2 | l3 | l4 |
|---|---|---|---|
| 105 cm | 55.95 cm | 69.07 cm | 14.36 cm |
The resulting robot working space for the validation set:
