A new neuro-FDS definition for indirect adaptive control of unknown nonlinear systems using a method of parameter hopping.

The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs.

Collision avoidance in commercial aircraft Free Flight via neural networks and non-linear programming.

In recent years there has been a great effort to convert the existing Air Traffic Control system into a novel system known as Free Flight. Free Flight is based on the concept that increasing international airspace capacity will grant more freedom to individual pilots during the enroute flight phase, thereby giving them the opportunity to alter flight paths in real time. Under the current system, pilots must request, then receive permission from air traffic controllers to alter flight paths.

Correction on 'Dynamical neural networks that ensure exponential identification error convergence'.

In the article by [Kosmatopoulos et al. (1997)] (Neural Networks 10(2) 299-314) the Theorem 4.1 was incorrect.

Dynamical Neural Networks that Ensure Exponential Identification Error Convergence.

Classical adaptive and robust adaptive schemes, are unable to ensure convergence of the identification error to zero, in the case of modeling errors. Therefore, the usage of such schemes to "black-box" identification of nonlinear systems ensures-in the best case-bounded identification error. In this paper, new learning (adaptive) laws are proposed which when applied to recurrent high order neural networks (RHONN) ensure that the identification error converges to zero exponentially fast, and even more, in the case where the identification error is initially zero, it remains equal to zero during the whole identification process.