变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swar...变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swarm optimization combined with back propagation neural network,PSO-BPNN)和自适应综合过采样(adaptive synthetic sampling,ADASYN)算法对变压器套管进行故障诊断。首先收集变压器套管的历史故障数据,建立具有明确故障类别的变压器套管油中溶解气体样本集,并通过ADASYN算法对原始数据中的少数类样本进行合成,得到平衡后的故障数据,然后将平衡后的油中溶解气体作为模型输入,故障状态作为标签输出,通过PSO-BPNN模型对变压器套管进行诊断,最后在原始样本集下使用反向传播神经网络(back propagation neural network,BPNN)、遗传结合反向传播神经网络(genetic combined with back propagation neural network,G-BPNN)算法、布谷鸟搜索结合反向传播神经网络(cuckoo search combined with back propagation neural network,CS-BPNN)算法以及PSO-BPNN模型对套管进行诊断。结果表明,针对变压器油纸套管绝缘状态进行故障诊断的多个模型中,基于ADASYN平衡数据后的PSO-BPNN模型和其他模型相比准确度最高,能有效减小小样本不平衡数据对诊断结果的影响,为判断变压器油纸套管绝缘性能提供了有效方法。展开更多
Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convi...Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.展开更多
Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is propo...Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.展开更多
In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electr...In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In MLP-BP NN five training methods used were a) Gradient Descent Back Propagation b) Levenberg-Marquardt c) Resilient Back Propagation d) Conjugate Learning Gradient Back Propagation and e) Gradient Descent Back Propagation with movementum.展开更多
文摘变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swarm optimization combined with back propagation neural network,PSO-BPNN)和自适应综合过采样(adaptive synthetic sampling,ADASYN)算法对变压器套管进行故障诊断。首先收集变压器套管的历史故障数据,建立具有明确故障类别的变压器套管油中溶解气体样本集,并通过ADASYN算法对原始数据中的少数类样本进行合成,得到平衡后的故障数据,然后将平衡后的油中溶解气体作为模型输入,故障状态作为标签输出,通过PSO-BPNN模型对变压器套管进行诊断,最后在原始样本集下使用反向传播神经网络(back propagation neural network,BPNN)、遗传结合反向传播神经网络(genetic combined with back propagation neural network,G-BPNN)算法、布谷鸟搜索结合反向传播神经网络(cuckoo search combined with back propagation neural network,CS-BPNN)算法以及PSO-BPNN模型对套管进行诊断。结果表明,针对变压器油纸套管绝缘状态进行故障诊断的多个模型中,基于ADASYN平衡数据后的PSO-BPNN模型和其他模型相比准确度最高,能有效减小小样本不平衡数据对诊断结果的影响,为判断变压器油纸套管绝缘性能提供了有效方法。
基金the National Natural Science Foundation of China (No.40671145)the Natural Science Foundation of Guangdong Province (Nos.04300504 and 05006623)and the Science and Technology Plan Foundation of Guangdong Province (Nos.2005B20701008,2005B10101028,and 2004B20701006).
文摘Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.
基金National Natural Science Foundation of China(60363087 ,90104005 and 60473023)
文摘Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.
文摘In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In MLP-BP NN five training methods used were a) Gradient Descent Back Propagation b) Levenberg-Marquardt c) Resilient Back Propagation d) Conjugate Learning Gradient Back Propagation and e) Gradient Descent Back Propagation with movementum.
