This paper proposes a self-tuning feedforward active noise control (ANC) system with online secondary path modeling. The stepsize parameters of the controller and modeling filters have crucial rule on the system performance. In literature, these parameters are adjusted by trial-and-error. In other words, they are manually initialized before system starting, which require performing extensive ex...

This talk will consider several types of adaptive control structure for nonlinear systems, based on the approximation of nonlinear functions by neural or fuzzy networks, which are also used to generate the control signals. The dynamic equations will be formulated as a generalisation of direct self-tuning or modelreference adaptation, in order to address the issues of parameter convergence and s...

With the advent of computational grids, networking performance over the wide-area network (WAN) has become a critical component in the grid infrastructure. Unfortunately, many high-performance grid applications only use a small fraction of their available bandwidth because operating systems and their associated protocol stacks are still tuned for yesterday’s network speeds. As a result, network...

In the context of fuzzy control, antecedent parameters are used to provide a segmentation of the state space so that different regions can be modeled appropriately. In Adaptive Critic methodologies, two modules (the critic and the controller) must properly segment the state space to insure good performance. In this paper, we explore the effects of tuning antecedent parameters that are shared be...

To overcome the disadvantages appeared in the application of some supermini underwater robots, a simple improved design framework is proposed. A mechanical improvement scheme is proposed. An easy handle, powerful and computer aided software system including the closed-loop control algorithm is developed for solving the shortcomings that are existed in the manual operation. The modeling of under...

Activated sludge wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior, however, he control of the dissolved oxygen level in the reactors plays an important role in the operation of the facility. For this reason a new pproach is studied in this paper using simulated case-study approach: model predictive control (MPC) has been applied to co...

This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, su...

The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm –one of the most representative evolutionary fuzzy systems for subgroup discovery– obtains precise and interpretable subgroups. However in the...

In this paper we present a new self-tuning procedure for PID controllers based on neuro-predictive control. A finite horizon optimal control problem is solved on-line, permitting to calculate the tuning parameters of the PID controller. The proposed method is implemented on a level-flow pilot plant and a comparison with conventional auto-tuning methods is also given.

Most recent schema matching systems assemble multiple components, each employing a particular matching technique. The domain user must then tune the system: select the right component to be executed and correctly adjust their numerous “knobs” (e.g., thresholds, formula coefficients). Tuning is skilland time-intensive, but (as we show) without it the matching accuracy is significantly inferior. ...