Improved DNN identifier based on takagi sugeno fuzzy systems

Several non-linear systems show complex behaviors. For example, some of those plants present a high degree of oscillations throughout the time. Adaptive algorithms used to approximate such difficult neural behaviors can show important definciencies. The differential network is not an exception. Indeed, when just one neural network is applied to get an adequate approximation, the identification error could be not so close to zero. One possible suggestion to solve this problem is to define a set of neuronal networks that works together. The members of such set will work each one on well defined trajectories subspaces of the uncertain system. In this paper, it is discussed how to combine the identification properties offered by the continuous neural network and the characteristic decision capabilities arised by fuzzy methods. The selection of which neural network is activated depends on decision achieved by a takagi-sugeno fuzzy system. The Chen circuit will be used to demonstrate the superior performance achieved by the suggested class of mixed neural network and fuzzy system, usually so-called neuro-fuzzy system.