Ahmed Saad Abdelhadi

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In this paper, a new improved search space boundary resizing method for an optimal model's parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the(More)
In this paper, standard genetic algorithms (SGAs) are presented to optimise the decentralised discrete PID parameters by three tuning approaches for a multivariable glass furnace with single loop interaction. At initial state, with the aid of control oriented model, the individual tuning approach without loop interaction is considered to categories the(More)
The hybrid traffic, a composite of elastic and inelastic data generated by delay-tolerant and real-time applications respectively, forms the bulk of what is carried over modern cellular systems. Since a smooth performance of real-time and delay-tolerant applications is tied to their distinctive needs for resources, networks’ intelligence about(More)
In this paper, a realistic excess oxygen model parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The realistic excess oxygen model is developed by three sub-model; air-fuel ratio conversion model, dynamic continuous transfer function and excess oxygen look-up table to characterise the real excess oxygen plant's(More)
In this paper, the potential of standard genetic algorithms (SGAs) are presented to optimise the discrete PID parameters for multivariable glass furnace. Control oriented models of each multivariable glass furnace; glass temperature and excess oxygen are used to optimise the discrete controller with personalised cost function and adjusted boundaries by(More)
Control of pH is a common problem faced in a range of industrial systems, such as chemical, biochemical industries and waste water treatment, due to its high nonlinearity. The main objective in this paper is to identify a pH model from real data by using an application of Local Linear Model Networks (LLMN) and subsequently develop a nonlinear control system(More)
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