An introduction to neural computing
An introduction to neural computing
Introduction to artificial neural systems
Introduction to artificial neural systems
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
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Most practical systems are inherently non-linear to some extent in their behaviour and for their cost effective, smooth and safe operation, optimised control systems based on the non-linear models are required. To this end many useful techniques such as the stochastic modelling, sliding mode control and adaptive identification and control have been proposed in the literature. However, the high cost of implementation, the inability to capture imprecision with the required level of tolerance, and the in-flexibility against distortions in the operating variables, make them less attractive. To this end new artificial intelligence based techniques such as fuzzy logic, neural networks and probabilistic reasoning, are becoming more and more popular. Among these techniques neural networks have an edge over the others, mainly because of their ability to process large amount of available data, subsequent to the development of some interpretable models for solving engineering problems. Moreover, the ability to capture the non-linearities of a real system accurately and the versatility in being able to accommodate with ease, the various conventional and advanced strategies within their structures, make them much more attractive. The problem becomes more computationally worse and uncontrollable when inverse of the system does not exist. This problem is resolved when neural network based techniques such as internal model control (IMC) are applied to the real systems. This paper outlines the application of neural networks based IMC methods for estimation/control of important input and output variables of a 0.5MW laboratory scale industrial furnace. The application involves inputs such as the airflow rate, swirl number and momentum ratio. The outputs include emission levels of oxides of nitrogen especially nitric oxide. The response to step and staircase inputs has been analysed. The results have been compared with standard linear quadratic controller. The control output of the IMC methods has resulted in almost similar steady state error performance to the linear quadratic regulator. Although the development process of the IMC method might take longer time because of the training and data arrangement but has the capability of readjustment after being developed.