Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in...
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Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature-control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is also compared to that of the traditional PID controller and;fuzzy logic controller (FLC) on the water bath temperature-control system. The three control schemes are compared through experimental studies with respect to set-points regulation, ramp-points tracking, and the influence of unknown impulse noise and large parameter variation in the temperature-control system. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes.
An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilitie...
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An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, kalman filter algorithm and extended kalman filter algorithms. A key feature of the new architecture is that a high-dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).
The amount of damping in power system oscillations plays an important role in the stability of the system. This paper presents a new application of linear kalmanfiltering for analysis of transient stability swings in...
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The amount of damping in power system oscillations plays an important role in the stability of the system. This paper presents a new application of linear kalmanfiltering for analysis of transient stability swings in large interconnected power systems in order to determine the damping constants and oscillation amplitude. It is shown that kalmanfiltering models are well suited for analyzing beat oscillations as well as noisy measurements. The effects of sampling rate, data window size and overall accuracy are also investigated. Results are reported for an actual recorded data set. II is concluded that the quality of estimates is degraded as the noise level is increased.
The Bayesian method is used to forecast with incomplete data. This involves the application of the kalmanfilter technique. It is shown that this is an appropriate procedure. Among many advantages, the kalmanfilter a...
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The Bayesian method is used to forecast with incomplete data. This involves the application of the kalmanfilter technique. It is shown that this is an appropriate procedure. Among many advantages, the kalman filter algorithm can generate forecasts with paucity of data, and it is possible to calculate the exact likelihood function involved. Several models are presented in their state space representations, and their uses in forecasting are discussed.
The paper is concerned with transputer implementations of three forms of the kalman filter algorithm. Two approaches to a concurrent realisation are considered, involving heuristic partitioning of the conventional seq...
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The paper is concerned with transputer implementations of three forms of the kalman filter algorithm. Two approaches to a concurrent realisation are considered, involving heuristic partitioning of the conventional sequential algorithm and proposing a new strategy for mapping the systolic array descriptions onto parallel processors. Real-time implementation results on transputer networks are presented and the relative performances evaluated in terms of speed, parallel processing efficiency and numerical accuracy for a test system. This attempts not only to quantify the performances of parallel kalmanfilters but also to address the more general issues involved in mapping algorithms onto architectures.
Harrison and Stevens (1976) give a general class of dynamic linear models which incorporates a stochastic structure for describing model components and which utilizes the kalman filter algorithm to update the system. ...
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Harrison and Stevens (1976) give a general class of dynamic linear models which incorporates a stochastic structure for describing model components and which utilizes the kalman filter algorithm to update the system. This paper derives conditions for the dynamic linear models to yield polynomial-projecting predictors which do not have limiting constraints imposed upon them. Some examples of structurally simple models which satisfy these conditions are given, and the use of a diagonal covariance matrix for the system disturbances is discussed.
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