Highly adaptive neural networks for adaptive neuro-control: the OWE architecture

The purpose of this work is to deal with real world applications where the size of the problem and the influence of contextual parameters can lead to inefficiency of control including neuro-control. Indeed, the size of real world problems is often very high, especially if numerous contextual parameters are used. This often leads to a prohibitive size for neural networks taken in their monolithic version. On the other hand, the absence of this context will destinate the work to a specific domain of application. These remarks are all the more accurate as we know that this contextual parameters generally have a continuous effect on the process and that they could be considered as specific modulators of the application. We present here a learning algorithm for neuro-controllers with reduced number of inputs. In fact, the contextual parameters are only used to estimate the synaptic weights of the neuro-controller with other NNs. Moreover, the present version is an online economic solution to this problem