Remarks on neural network controller using different sigmoid functions

Many studies such as Kawato´s work (1987) have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. Most of them used a fixed shape sigmoid function. We have confirmed that it is useful to change the sigmoid function shape to improve the nonlinear mapping capability of neural network controllers. This paper introduces the a new concept for autotuning sigmoid function shapes of neural network servo controllers. Three types of tuning method are proposed in order to improve the nonlinear mapping capability. The first type uses a uniform sigmoid function shape. With the second type, the sigmoid function shapes within one layer are the same and the shapes are tuned layer by layer is tuned. With the third type, the sigmoid function shape of each neuron is different and is tuned individually. Their characteristics are confirmed by simulation