Papers by Keyword: Annealing Algorithm

Abstract: Hybrid technology is widely recognized as the most effective measure to reduce the fuel consumption in Load-Haul-Dump (LHD). Using a hybrid power system on LHD is proposed. This comprises an internal combustion engine and super capacitor. LHD has the characteristics of duty cycle. Based on the characteristics we get the hydraulic power demand,electric transmission power demand. We propose an energy control strategy to reasonably distribute power of hybrid energy sources. With the help of control strategy, super capacitor can be restricted to work in a pre-defined range of SOC characterized by higher efficiency region. We also analyze the super capacitor charging/discharging process. In order to realize the fuel economy fullest, we optimize the design parameter using the simulated annealing algorithm. The simulation results show that improvement in fuel economy was about 11.3% better than that of before the optimization.

Abstract: In this paper, a new mathematical combined model and the corresponding solution algorithm were proposed by analyzing the characteristics of services resource combination in cloud manufacturing. Aiming at avoiding problems of uncertainty, coarse-grained, diversity and dynamic in the process of services resource combination, a hierarchical model based on the hierarchical manufacturing implementation processes was firstly proposed. Then, quality of service (QoS) has been chosen to evaluate effects of services combination. Finally, a annealing algorithm was developed to solve the proposed model. Simulation experiment results prove the validity of the model and algorithm.

Abstract: Rolling temperature is an important factor affecting mechanical properties of hot rolled strip significantly. Traditional techniques cannot meet higher precision control imperatives. In the present work, a novel knowledge-based system has been developed for the temperature prediction in hot strip mills. Neural network has been used for this purpose, which is an intelligent technique that can solve nonlinear problem of temperature control by learning from the samples. Furthermore, an annealing robust learning algorithm was presented to adjust the hidden node parameters as well as the weights of the adaptive neural networks. Simulations in a multi-object mode have been carried out to verify the effectivity of new neural optimization system. Calculation results confirm the feasibility of this approach and show a good agreement with experimental values obtained from a steel plant.