Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last fe...
Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last few years. This paper examines one such algorithm, the partial least squares (PLS), and shows how the basic principles of this linear technique can be extended into the nonlinear domain via the application of radial basis function (RBF) neuralnetworks. Results showing the successful application of these methods to fault detection in a validated model of an industrial overheads condenser and reflux drum plant are also given.
The article describes the concept and performance of neuro fuzzy modelling and neuro fuzzy model based control. The technique has been applied to a highly nonlinear neutralisation process. It is shown that the neuro f...
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The article describes the concept and performance of neuro fuzzy modelling and neuro fuzzy model based control. The technique has been applied to a highly nonlinear neutralisation process. It is shown that the neuro fuzzy model is easy to interpret and provides accurate predictions. The proposed neuro fuzzy model based control strategy utilises local incremental models which can naturally eliminate steady state offsets. It is shown that the neuro fuzzy model based controller gives much improved performance compared to that of a linear model based controller.
The process under investigation in this work is the integrated dry route (IDR) process of British Nuclear Fuels plc. (BNFL), which is a nuclear fuel processing plant, where non-catastrophic faults are known to occur a...
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The process under investigation in this work is the integrated dry route (IDR) process of British Nuclear Fuels plc. (BNFL), which is a nuclear fuel processing plant, where non-catastrophic faults are known to occur and a reliable early fault diagnosis scheme was required for operator advice. This paper describes the application of artificial neural network techniques to the diagnosis of non-catastrophic faults in the IDR process which operates at a few different operating points. The techniques involved developing methods to preprocess the data by statistical scaling, reducing the neural network input space using principal component analysis and training and testing the neuralnetworks. Results are presented to illustrate the performance of the developed scheme on application to the IDR process data.
Over the last thirty years, since Zadeh first introduced fuzzy set theory, there has been widespread interest in the real-time application of fuzzy logic, particularly in the area of control. Recently, there has been ...
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Over the last thirty years, since Zadeh first introduced fuzzy set theory, there has been widespread interest in the real-time application of fuzzy logic, particularly in the area of control. Recently, there has been considerable interest in the development of dedicated hardware implementations which facilitate high speed processing. However, the main drawback of such an approach is that it is only cost effective for high-volume applications. A more feasible methodology for lower volume problems demands the application of general-purpose or programmable hardware such as the Xilinx FPGAs. There has been a similar trend in the area of neuralnetworks, as initial research employed software simulations but more recent interest has investigated hardware implementations. FPGAs are becoming increasingly popular for prototyping and designing complex hardware systems. The structure of an FPGA can be described as an "array of blocks" connected together via programmable interconnections. The main advantage of FPGAs is the flexibility that they afford. An engineer can change and refine a device's design by exploiting the device's reprogrammability. Xilinx introduced the world's first FPGA, the XC2064, in 1985. This contained approximately 1000 logic gates. Since then, the gate density of Xilinx FPGAs has increased 25 times. There has been a lot of recent interest in the FPGA realisation of fuzzy systems. Similarly there are a number of FPGA implementations of neuralnetworks reported in the literature. However. this paper provides a report on the implementation of both architectures and also offers a comparison with the hybrid structure.
This paper proposes an ANN-based classification approach for fast transient stability assessment of large power systems. A two-level classifier incorporating two feed-forward ANNs is built to obtain a stability index ...
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ISBN:
(纸本)0852969120
This paper proposes an ANN-based classification approach for fast transient stability assessment of large power systems. A two-level classifier incorporating two feed-forward ANNs is built to obtain a stability index for security classification using some general abstract post-fault attributes as its inputs. The ANNs are trained by a newly-developed semi-supervised learning algorithm. The proposed approach can not only distinguish whether a power system is stable or unstable based on the specific post-fault attributes but also provide a relative stability quantifier. The numerical results on applications to the 10-unit New England power system demonstrate the validity of the proposed approach for transient security assessment.
The proceedings contains 17 papers from the advances in neuralnetworks for control and systems symposium held at the systems Technology Research Centre, Daimler-Benz AG, Berlin from May 25 to 27, 1994. Topics discuss...
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The proceedings contains 17 papers from the advances in neuralnetworks for control and systems symposium held at the systems Technology Research Centre, Daimler-Benz AG, Berlin from May 25 to 27, 1994. Topics discussed include constructive learning algorithms;learning systems;semi-empirical modelling of nonlinear dynamical systems;neuralnetworks for industrial process control;Kohonen feature maps;batch reactor temperature control;interpolation memories;gradient-based training algorithm;stability theory;adaptive neurofuzzy systems;constructive learning and nonlinear adaptive clustering.
The proceedings contains 11 papers from the 1996 ieecolloquium on Modelling and Signal Processing for Fault Diagnosis. Topics discussed include: fault detection using neuralnetworks;signal processing and statistical...
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The proceedings contains 11 papers from the 1996 ieecolloquium on Modelling and Signal Processing for Fault Diagnosis. Topics discussed include: fault detection using neuralnetworks;signal processing and statistical process control (SPC);pattern recognition;condition monitoring systems;fault diagnosis of engineering systems;two-stage adaptive line enhancer (ALE);deconvolution;robust fault detection observer (RFDO);and fault detection and isolation (FDI).
The proceedings contains 5 papers. Topics discussed include safety related systems, software for programmable logic controllers, safety aspects in expert systems, neuralnetworks and fuzzy logic, control and safety st...
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The proceedings contains 5 papers. Topics discussed include safety related systems, software for programmable logic controllers, safety aspects in expert systems, neuralnetworks and fuzzy logic, control and safety strategy in water industry.
The basic concepts of PID control can be generalized within the same structure but allowing for the control of complicated dynamic systems using advanced control design algorithms. This structure arises naturally from...
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The basic concepts of PID control can be generalized within the same structure but allowing for the control of complicated dynamic systems using advanced control design algorithms. This structure arises naturally from the system description and does not need to be imposed artificially. Recent advances in Local Model networks give a neat extension of the basic (generalized) PID structure to handle nonlinear or time-varying systems.
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