Based on the fact that the ball mill load in power plant is hard to detect effectively, an online local learning improved weighted least square support vector machine (WLSSVM) soft-sensing method is proposed form impr...
详细信息
ISBN:
(纸本)9789881563811
Based on the fact that the ball mill load in power plant is hard to detect effectively, an online local learning improved weighted least square support vector machine (WLSSVM) soft-sensing method is proposed form improving the prediction accuracy and adaptive ability of the soft-sensing model. Firstly, a similarity measurement criterion of data samples based on the samples distance and trend information is designed, and the modeling neighborhood dataset for local model is obtained by using two-step search strategy. Secondly, for the purpose of improving the prediction performance of the local model, an improved WLSSVM local model which can reflect the similarity and the error information of the modeling data is proposed, and the parameters of the local model is optimized by using improved differential evolution algorithm with mutation operation. Thirdly, in order to control the data size of the historical database, the active update strategy is adopted to realize the selective preservation of the new data. Finally, the simulation experiments are carried out based on the actual operation data of the coal pulverizing system. Simulation results show that compared with the standard LSSVM model, the proposed soft-sensing model has better prediction accurate and can predict the power plant ball mill load effectively.
Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. However man-made selecting the structure of BP neural network has...
详细信息
Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. However man-made selecting the structure of BP neural network has blindness and expends much time, so differentialevolution (DE) algorithm was adopted to automatically search BP neural network weight matrix and threshold matrix. In order to improve the convergence of DE algorithm, a chaotic sequence based on logistic map was introduced to self-adaptively adjust mutation factor. Furthermore, a self-adapting crossover probability factor was presented to improve the population’s diversity and the ability of escaping from the local optimum. Study case shows that, compared with groundwater level prediction model based on traditional BP neural network, the new prediction model based on DE and BP neural network can greatly improve the convergence speed and prediction precision.
The paper analyzes the character of distribution network, utilizes the multi-objective differentialevolutionalgorithm to carry on selecting and location optimization about the automatic equipment, takes switch as co...
详细信息
ISBN:
(纸本)9781424449347
The paper analyzes the character of distribution network, utilizes the multi-objective differentialevolutionalgorithm to carry on selecting and location optimization about the automatic equipment, takes switch as code of the chromosome gene, in each evolutionary generation, divides the community into three sub-communities which execute variation, crossover, fitness evaluation and select operation based on three factors respectively: Energy not supply (ENS), communication optical fiber cost, automation device invests. Then combine best individual into one unit, performances evolution operation on the next generation. By iterate process to obtain the best network scheme of distribution network automation planning finally. This paper analyzed the fault mode and consequence analytic method (FMEA) in the project usability, and made an improvement for this method, it means according to actual transmission capacity limit of the circuit, carry on verification of the load transfers through the flow calculation. As an auxiliary derision support system, the paper adopts visualization technology of based on the AUTOCAD, demonstrates the plan result on the geography background map, finally introduces the real distribution network application as an example.
暂无评论