  We propose a gradient ascent algorithm for quaternion multilayer perceptron
(MLP) networks based on the cost function of the maximum correntropy criterion
(MCC). In the algorithm, we use the split quaternion activation function based
on the generalized Hamilton-real quaternion gradient. By introducing a new
quaternion operator, we first rewrite the early quaternion single layer
perceptron algorithm. Secondly, we propose a gradient descent algorithm for
quaternion multilayer perceptron based on the cost function of the mean square
error (MSE). Finally, the MSE algorithm is extended to the MCC algorithm.
Simulations show the feasibility of the proposed method.
