The civil engineering community is currently moving towards the continuous monitoring of civil structures in order to forecast their unavoidable failure with enough precision. So-called smart technologies seem to be well adapted to this specific task. For a civil structure, such as a bridge or a dam, a monitoring smart system often includes a set of sensors, whose data is passed onto a controll...

Neural networks can be classified into recurrent and nonrecurrent categories. Nonrecurrent (feedforward) networks have no feedback elements; the output is calculated directly from the input through feedforward connections. In recurrent networks the output depends not only on the current input to the network, but also on the current or previous outputs or states of the network. For this reason, ...

Neural networks are trained for balancing 1 and 2 poles attached to a cart on a xed track. For one variant of the single pole system , only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a xed architecture and using a new version of cellular encoding that evolves an application speciic architectur...

In discussing spasticity and its pathophysiology we presented evidences for cellular alteration and their neurocontrol. We outline clinical practice of recognizing and describing spasms and spasticity in order to emphasize residual descending influence to the spinal cord. Finally, we provide published evidences that such residual descending influence can be exerted under an attempt of an altere...

Overview material for the Special Session (Tuning Fuzzy Controllers Using Adaptive Critic Based Approximate Dynamic Programming) is provided. The Dual Heuristic Programming (DHP) method of Approximate Dynamic Programming is described and used to the design a fuzzy control system. DHP and related techniques have been developed in the neurocontrol context but can be equally productive when used w...

Within a simple test-bed, application of feed-forward neurocontrol for short-term planning of robot trajectories in a dynamic environment is studied. The action network is embedded in a sensorymotoric system architecture that contains a separate world model. It is continuously fed with short-term predicted spatio-temporal obstacle trajectories, and receives robot state feedback. The action net ...

Based on a power-series approximation method, recurrent neural networks (RNN) are proposed for real-time synthesis and auto-tuning of feedback controllers for nonlinear output regulation systems. The proposed neurocontrol approach represents a novel application of recurrent neural networks to the nonlinear output regulation problem. The proposed approach completely inherits the stability and as...