The reliability analysis of a component or a system is an important concept in almost all engineering disciplines. In general, fuzzy sets are used to analyze the system reliability. The system reliability may be forme...
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The reliability analysis of a component or a system is an important concept in almost all engineering disciplines. In general, fuzzy sets are used to analyze the system reliability. The system reliability may be formed as linear or nonlinearprogramming with cost function in fuzzy environment. To analyze the fuzzy system reliability, the reliability of each component of the system is represented as fuzzy number in nature. In this paper, we have presented the parallel system model with fuzzy cost function to evaluate the maximum reliability subject to minimum cost using trapezoidal fuzzy number and to find out the Co-efficient of Variance for each membership function. A numerical example is given to illustrate the model for nonlinearprogramming and to evaluate the system reliability.
The multi-input multi-output servomechanism concept is applied to conventional tie line bias control (TBC) as an example of decentralised automatic generation control (AGC); to a centralised form of TBC and to another...
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The multi-input multi-output servomechanism concept is applied to conventional tie line bias control (TBC) as an example of decentralised automatic generation control (AGC); to a centralised form of TBC and to another centralised AGC scheme. The tracking properties of the AGC servocontroller and decoupling from higher levels of control are explored together with improved stability possibilities in the case of centralised coordination.
This communication gives a brief description of a practical penalty function approach to the solution of optimal control problems involving constraints on the state variables. The work is motivated by a wish to retain...
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This communication gives a brief description of a practical penalty function approach to the solution of optimal control problems involving constraints on the state variables. The work is motivated by a wish to retain some benefits of the Pontryagin approach to trajectory optimization when certain in-flight constraints are included. A single computational example is used to demonstrate the viability of the method.
In this paper, we put forward a new hybrid methodology to generate forecasts of time series. Indeed, the proposed forecaster is a HWCF that integrates the following techniques: wavelet decomposition;ARIMA models;SVRs;...
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In this paper, we put forward a new hybrid methodology to generate forecasts of time series. Indeed, the proposed forecaster is a HWCF that integrates the following techniques: wavelet decomposition;ARIMA models;SVRs;wavelet combination of forecasts;and non-linear programming. Basically, the HWCF is able to capture, simultaneously, linear and non-linear auto-dependence structures exhibited by a time series, which are represented, at time t, by both the linear and non-linear combined forecasts: L-C,L-t and N-C,N-t, respectively. After obtaining the combined forecasts L-C,L-t and N-C,N-t, they are summed (i. e., L-C,L-t + N-C,N-t = yh,t), producing the hybrid forecast yh,t, for each instant t. The numerical results show that HWCF achieved relevant accuracy gains in forecasting process of the annual time series of sunspot, when comparing with other ten competitive forecasters.
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