摘要 :
To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network's ability of identifying comp...
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To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network's ability of identifying complex nonlinear systems, a neural network identification model of MCFC stack is developed based on the sampled input-output data. Also, a novel online fuzzy control procedure for the temperature of MCFC stack is developed based on the fuzzy genetic algorithm (FGA). Parameters and rules of the fuzzy controller are optimized. With the neural network identification model, simulation of MCFC stack control is carried out. Validity of the model and the superior performance of the fuzzy controller are demonstrated.
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A genetic algorithm (GA) and a hybrid genetic algorithm (HGA) were used for optimal scheduling of public vehicles based on their actual operational environments.The performance for three kinds of vehicular levels were compared usi...
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A genetic algorithm (GA) and a hybrid genetic algorithm (HGA) were used for optimal scheduling of public vehicles based on their actual operational environments.The performance for three kinds of vehicular levels were compared using one-point and two-point crossover operations.The vehicle scheduling times are improved by the intelligent characteristics of the GA.The HGA,which integrates the genetic algorithm with a tabu search,further improves the convergence performance and the optimization by avoiding the premature convergence of the GA.The results show that intelligent scheduling of public vehicles based on the HGA overcomes the shortcomings of traditional scheduling methods.The vehicle operation management efficiency is improved by this essential technology for intelligent scheduling of public vehicles.
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A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA...
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A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.
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An aerodynamic design optimization platform (ADOP) has been developed. The numerical optimization method is based on genetic algorithm (GA), Pareto ranking and fitness sharing technique. The platform was used for design optimizati...
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An aerodynamic design optimization platform (ADOP) has been developed. The numerical optimization method is based on genetic algorithm (GA), Pareto ranking and fitness sharing technique. The platform was used for design optimization of the stator of an advanced transonic stage to seek high adiabatic efficiency. The compressor stage efficiency is increased by 0.502% at optimal point and the stall margin is enlarged by nearly 1.0% at design rotating speed. The flow fields of the transonic stage were simulated with FINE/Turbo software package. The optimization result indicates that the optimization platform is effective in 3D numerical design optimization problems.
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Networked rural electrification is an alternative approach to accelerate rural electrification.Using satellite photos and GIS tools,an electrical distribution network is used to connect villages and properly located generation fac...
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Networked rural electrification is an alternative approach to accelerate rural electrification.Using satellite photos and GIS tools,an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost.To design the network,optimal paths connecting all node-pairs are identified,followed by finding a network topology that minimizes cost.Earlier work has illustrated that A*(A-star,an optimal path-finding algorithm)is inefficient for this application due to the complex topography in rural areas.The multiplier-accelerated A*(MAA*)algorithm overcomes key performance issues,but,like A*,produces only one path connecting each node-pair.Relying on one path increases project risk because adverse conditions,such as inaccurate GIS estimation,unexpected soil conditions,land-rights disputes,political issues,etc.can occur during implementation.In this paper,a hybrid path-finding method combining genetic algorithm and A*/MAA*algorithm is proposed.The proposed method provides a family of near-optimal paths instead of a single optimal path for routing.A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available,and flexibly change network topology before or during implementation with minimal impact on project cost.
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In order for optical interconnection technologies to be incorporated into the next-generation parallel computers, new optoelectronic computer-aided design, integration, and packaging technologies must be investigated. One of the k...
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In order for optical interconnection technologies to be incorporated into the next-generation parallel computers, new optoelectronic computer-aided design, integration, and packaging technologies must be investigated. One of the key issues in designing is the system volume, which is determined by maximum interconnection distance (MID) between PEs. A novel 2-D genetic algorithm was presented in this paper at the first time, and used to solve the placement of twin-butterfly multistage networks based on transmissive physical model. The experiment result shows that this algorithm case works better than other algorithm cases.
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