The design of Digital Finite Impulse Response (FIR) digital band-pass filter using two Heuristic Optimization Technique have been implemented. Digital FIR Filters are better than Infinite Impulse Response Filters due ...
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ISBN:
(纸本)9781467375429
The design of Digital Finite Impulse Response (FIR) digital band-pass filter using two Heuristic Optimization Technique have been implemented. Digital FIR Filters are better than Infinite Impulse Response Filters due to their stability and having linear phase. This paper explores the two heuristic optimization techniques namely Particle Swarm Optimization and differentialevolution. The evaluation of performance of DE algorithm and PSO algorithm has been done and results performs have been compared on the basis of their control parameters. The achieved results show that the differential evolution algorithm better than that of Particle Swarm Optimization in terms of achieved magnitude error and ripples in pass-band and stop-band.
When using BP model to classify speech feature signal, the initial weight and threshold of BP model randomly causes the model to fall into local minimum. A differential evolution algorithm is proposed to optimize the ...
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ISBN:
(数字)9781538618035
ISBN:
(纸本)9781538618042
When using BP model to classify speech feature signal, the initial weight and threshold of BP model randomly causes the model to fall into local minimum. A differential evolution algorithm is proposed to optimize the weight and threshold parameters of BP model, then establishing a classification model of speech feature signal based on differentialevolution BP neural network algorithm. The classification models are trained and tested using the speech feature signals of four different kinds of music, folk songs, zither, rock and pop music. The test results show that compared with the traditional BP model, the improved BP algorithm model, the speech feature signal classification model based on differentialevolution BP neural network is better in operation stability and classification accuracy.
A novel inverse design method is established based on enhanced RBF neural network and improved differential evolution algorithm. This method combines some advantages of inverse design and optimization. The inverse des...
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ISBN:
(纸本)9781479917983
A novel inverse design method is established based on enhanced RBF neural network and improved differential evolution algorithm. This method combines some advantages of inverse design and optimization. The inverse design problems are transformed into optimization problems to some extent and the dependence on reasonable target pressure distribution is reduced. With enhanced RBF neural network, the calculation efficiency is improved. The application in supercritical wing design shows that this method is reasonable and can be used to research the effect of pressure distribution. The improvement of the drag divergence characteristic is owing to the change of shock location.
This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a ...
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ISBN:
(纸本)9781467356688
This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that the grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. The load not dispatched by the operator is not known with certainty, and is represented as a stochastic parameter in the optimization problem. Consumption restrictions associated with flexible demand are modeled by equality energy constraints. The performance of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, is presented.
Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...
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Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization (PSO) algorithm based on the differentialevolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
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