H. Ichihashi et al., A NEUROFUZZY APPROACH TO VARIATIONAL-PROBLEMS BY USING GAUSSIAN MEMBERSHIP FUNCTIONS, International journal of approximate reasoning, 13(4), 1995, pp. 287-302
In this paper we propose a neurofuzzy direct solution method for varia
tional problems in which the cost function of an integral form is mini
mized. We deal with two nonlinear systems; one is a direct drive (DD)
manipulator system and the other is a trailer-truck system. The DD man
ipulator system is described by a continuous-time dynamical model, and
the trailer-truck system is described by a discrete-time dynamical mo
del. The problem is to find trajectories which minimize the cost funct
ion of an integral form. The trajectories of state variables and input
variables are represented by fuzzy models that consist of Gaussian me
mbership functions. The networks of Gaussian functions are trained by
the steepest-descent method to minimize the cost function. The propose
d neurofuzzy approach provides a direct solution method of the variati
onal problems by using Gaussian functions. The function is regarded as
a simplified fuzzy reasoning model and called neurofuzzy.