In this paper, we aims to improve stability of learning processes by the spikeprop algorithm. We proposed the method that reduce the increase of the error in learning processes. It repeats two steps: (1) original Spik...
详细信息
ISBN:
(纸本)9781424496365
In this paper, we aims to improve stability of learning processes by the spikeprop algorithm. We proposed the method that reduce the increase of the error in learning processes. It repeats two steps: (1) original spikeprop algorithm, and (2) use a linear search in the steepest descent direction only if the first step is failed. Some experimental results shows the improvement of learning processes.
Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC) is usually used to improve t...
详细信息
Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC) is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs) are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR). Furthermore, enhancements to the spikeprop learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks. (C) 2012 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B. V. All rights reserved.
Short-term wind speed forecasting plays an important role in the daily power system operation. Therefore, this paper presents a novel model based on spiking neural network (SNN) used spike response model (SRM). Furthe...
详细信息
ISBN:
(纸本)9781538671740
Short-term wind speed forecasting plays an important role in the daily power system operation. Therefore, this paper presents a novel model based on spiking neural network (SNN) used spike response model (SRM). Further, to achieve both smaller training errors and higher precision forecasting, the basic spikeprop learning algorithm is improved by adaptively adjusting the learning rate and adding momentum items. Then, this paper selects the actual sampling data from a wind farm to verify the effectiveness and advantages of the proposed model.
暂无评论