Greenhouses provide a protected environment in which crops can be grown under a tightly controlled climate. The efficiency of plant production in greenhouses depends significantly on the adjustment of optimal climate growth conditions to achieve high production at low expense, good quality and low environment load. To achieve these goals several components,… (More)
Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back… (More)
A robotic system at the discrete event level requires a suitable discrete event controller that guarantees coordination and dead lock avoidance. It requires also a robust system of fault detection and localization based on minimum feedback information.
Event detectability is the corner stone of constructing reliable diagnosers for Petri net (PN) models. In some PN models, the transition firing sequences are not detectable based on their outputs and structural information only. This paper introduces a novel diagnoser to overcome such problems. The developed diagnoser depends not only on the output and the… (More)
The Genetic Algorithm (GA) has the superiority over classical optimization algorithms in finding the optimal (global) solution in multi-parameter search space. Because it simultaneously evaluates many points in the parameters space, it is more likely to converge toward the global solution. It need not assume that the search space differentiable or… (More)
In this paper, the idea of fertilizing fuzzy neural networks with wavelets is borrowed and enhanced to introduce a novel neural network named Auto Regressive eXogenous Local Model (ARX-LM) network. The enhanced network has a set of notable features compared with previous published fertilized networks. These features can be summarized as follows. First, the… (More)
Elman network is a class of recurrent neural networks used for function approximation. It has a set of global sigmoid functions at its hidden units. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This is due to the fact that the universes of discourse of the… (More)