Supervised learning in continuous feedback neural network

Two supervised learning algorithms for the continuous feedback neural network (CFNN) have been derived which can learn multiple patterns correctly. It is noted that, when the parameters of the CFNN are determined, its dynamic behavior is completely determined, so determining the weight coefficients of the CFNN is a crucial step for using the CFNN. For a CFNN used as associative memory, one wants the required patterns to be the equilibrium points of the CFNN.<>